U.S. patent application number 13/492825 was filed with the patent office on 2012-12-13 for auto-focus image system.
Invention is credited to Hiok Nam TAY.
Application Number | 20120314960 13/492825 |
Document ID | / |
Family ID | 42470708 |
Filed Date | 2012-12-13 |
United States Patent
Application |
20120314960 |
Kind Code |
A1 |
TAY; Hiok Nam |
December 13, 2012 |
AUTO-FOCUS IMAGE SYSTEM
Abstract
An auto-focus image system includes a focus signal generator and
a pixel array coupled thereto that captures an image that includes
a plurality of edges. The generator computes a focus signal from a
plurality of edge-sharpness measures, each measured from and
contributed by a different edge as a quantity with a unit that is a
power of a unit of length, such as a distance in the edge, an area,
or an even-order central moment. A relative weight of the
contribution by an edge is reduced depending on at least a pair of
shape measures, each being computed from a plurality of sample-pair
differences of the edge. One may be the edge-sharpness measure. The
weight may be zero if the pair of shape measures falls outside a
predetermined region. At least one symmetrical sequence of
gradients exists such that an edge with it has reduced relative
weight.
Inventors: |
TAY; Hiok Nam; (Singapore,
SG) |
Family ID: |
42470708 |
Appl. No.: |
13/492825 |
Filed: |
June 9, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/IB2011/052515 |
Jun 9, 2011 |
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13492825 |
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Current U.S.
Class: |
382/199 |
Current CPC
Class: |
G02B 7/36 20130101; G06T
2207/10004 20130101; G06T 5/003 20130101; H04N 5/23212 20130101;
G06K 9/4604 20130101; G03B 13/36 20130101; H04N 5/3696 20130101;
H04N 5/232123 20180801; H04N 5/232945 20180801; H04N 9/04515
20180801; H04N 5/232935 20180801; H04N 5/23293 20130101 |
Class at
Publication: |
382/199 |
International
Class: |
G06K 9/48 20060101
G06K009/48 |
Claims
1. A method for generating a focus signal from a plurality of edges
of an image of a scene to indicate a degree of image sharpness,
comprising: evaluating in a computing device a first measure and a
second measure on an edge detected from the image to find a first
value and a second value, respectively; and, determining by use of
at least the first and second values a relative extent to which the
edge weighs in contributing to the focus signal as compared with
other edges that contribute to the focus signal, wherein the first
and second measures of any edge are each a quantity that depends on
at least two image-sample differences, each image-sample difference
being a difference between a pair of samples of image data, the
samples being from a sequence of image data samples across said any
edge, wherein the determining is not based on measuring an extent
to which a sequence of gradients across the edge lacks reflection
symmetry.
2. The method of claim 1, wherein the evaluating the first measure
does not depend upon detection of another edge.
3. The method of claim 1, wherein a 20% decrease in an illumination
of the scene does not result in a difference whether the edge is
omitted or allowed to contribute to the focus signal.
4. The method of claim 1, wherein the determining determines the
relative extent by comparing the first value with a predetermined
criterion that depends on at least the second value.
5. The method of claim 4, further comprising: omitting or
deemphasizing the edge in the generating of the focus signal where
the edge does not meet the predetermined criterion.
6. The method of claim 1, wherein the determining determines the
relative extent as a function of at least the first and second
measures.
7. The method of claim 6, wherein the relative extent is a weight
for a contribution of the edge towards the focus signal.
8. The method of claim 1, wherein any edge that contributes to the
focus signal contributes an edge-sharpness measure that is a
quantity computed from a plurality of samples of image data within
a predetermined neighborhood of said any edge.
9. The method of claim 8, wherein the edge-sharpness measure is
also the second measure.
10. The method of claim 8, wherein the edge-sharpness measure is
neither the first measure nor the second measure.
11. The method of claim 10, wherein the edge-sharpness measure of
said any edge is not evaluated where said any edge is omitted from
the generating of the focus signal.
12. The method of claim 10, wherein the edge-sharpness measure of
said any edge is a width of a predefined portion of said any edge
predefined according to a predetermined manner.
13. The method of claim 10, wherein the edge-sharpness measure of
said any edge is a peak gradient value of said any edge divided by
a contrast across said any edge or across a predefined portion of
said any edge.
14. The method of claim 10, wherein the edge-sharpness measure is a
second moment of gradients in the sequence of gradients.
15. The method of claim 1, wherein each edge consists of a
plurality of pixels arrayed contiguously in a first direction and
is detected by an edge detector.
16. The method of claim 15, wherein the edge detector detects said
each edge using a first-order edge detection operator.
17. The method of claim 1, wherein the first and second measures
are mutually independent in the sense that neither can be computed
from the other without further involving at least one sample of
image data from a predetermined neighborhood of the edge.
18. The method of claim 1, wherein the first and second measures
and the edge-sharpness measure of any edge are computed from a
plurality of samples of image data within a predetermined
neighborhood of said any edge.
19. The method of claim 8, wherein the edge-sharpness measure of
any edge has a unit that is a power of a unit of length, given that
each sample of image data has a unit that is a unit of energy, that
a difference between any pair of samples of image data divided by a
distance between the samples has a unit that is a unit of energy
divided by a unit of length, that a distance between gradients and
a count of pixels both have a unit that is a unit of length, that a
gradient value has a unit that is a unit of energy divided by a
unit length, and that normalized gradient values are unitless.
20. The method of claim 8, wherein the edge-sharpness measure of
the edge is not evaluated where the edge does not contribute to the
generating of the focus signal.
21. The method of claim 1, wherein the first and second measures
are both not affected by scaling the plurality of samples of image
data by a non-zero scaling factor while other samples of image data
are not scaled.
22. The method of claim 1, wherein the first and second measures
are both affected by scaling the plurality of samples of image data
by a non-zero scaling factor.
23. The method of claim 1, wherein there is a spurious sequence of
gradients having perfect reflection symmetry such that the
determining necessarily reduces the relative extent where the edge
has the spurious sequence of gradients across itself.
24. The method of claim 23, wherein the spurious sequence of
gradients is {0, 0.2, 0.2, 0.7, 0.7, 1, 0.7, 0.7, 0.2, 0.2, 0}.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation-in-part of International
Patent Application No. PCT/IB2011/052515 filed on Jun. 9, 2011.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The subject matter disclosed generally relates to
auto-focusing electronically captured images.
[0004] 2. Background Information
[0005] Photographic equipment such as digital cameras and digital
camcorders may contain electronic image sensors that capture light
for processing into still or video images, respectively. Electronic
image sensors typically contain millions of light capturing
elements such as photodiodes.
[0006] Many image capturing devices such as cameras include an
auto-focusing system. The process of auto-focusing includes the
steps of capturing an image, processing the image to determine
whether it is in focus, and if not, generating a feedback signal
that is used to vary a position of a focus lens ("focus position").
There are two primary auto-focusing techniques.
[0007] The first technique involves contrast measurement, the other
technique looks at a phase difference between a pair of images. In
the contrast method the intensity difference between adjacent
pixels is analyzed and the focus is adjusted until a maximum
contrast is detected. Although acceptable for still pictures the
contrast technique is not suitable for motion video.
[0008] The phase difference method includes splitting an incoming
image into two images that are captured by separate image sensors.
The two images are compared to determine a phase difference. The
focus position is adjusted until the two images match. The phase
difference method requires additional parts such as a beam splitter
and an extra image sensor. Additionally, the phase difference
approach analyzes a relatively small band of fixed detection
points. Having a small group of detection points is prone to error
because noise may be superimposed onto one or more points. This
technique is also ineffective if the detection points do not
coincide with an image edge. Finally, because the phase difference
method splits the light the amount of light that impinges on a
light sensor is cut in half or even more. This can be problematic
in dim settings where the image light intensity is already low.
BRIEF SUMMARY OF THE INVENTION
[0009] An auto-focus image system includes a pixel array and a
focus signal generator coupled to the pixel array. The pixel array
captures an image that includes a plurality of edges. The generator
generates a focus signal that is computed from a plurality of
edge-sharpness measures, each being measured from and contributed
by a different one of the plurality of edges. The edge-sharpness
measure has a unit that is a power of a unit of length. The
edge-sharpness measure of an edge may be a distance in the edge.
Or, it may be an area under a gradient profile of the edge. Or, it
may be a central moment of gradients or second derivatives of the
edge. The generator may reduce a relative extent to which an edge
contributes to the focus signal using two or more shape measures of
the edge. Each shape measure is computed from a plurality of
sample-pair differences, where each sample-pair difference is a
difference between a pair of samples of image data within a
predetermined neighborhood of the edge. One of the shape measures
may be the edge-sharpness measure itself. An edge may be prevented
from contributing to the focus signal, i.e. given a weight of zero,
if an n-tuple of the two or more shape measures lies outside a
predetermined region, where n is the number of the two or more
shape measures. There is at least one perfectly symmetrical
sequence of gradients that if the edge has the sequence across
itself then the edge is either rejected or has its relative extent
reduced.
[0010] Summarized below are enumerated aspects of the
invention.
[0011] 1. A method for generating a focus signal from a plurality
of edges of an image of a scene to indicate a degree of image
sharpness, comprising:
[0012] evaluating in a computing device a first measure and a
second measure on an edge detected from the image to find a first
value and a second value, respectively; and,
[0013] determining by use of at least the first and second values
to reduce a relative extent that the edge weighs in contributing to
the focus signal as compared with other edges that contribute to
the focus signal,
[0014] wherein the first and second measures of any edge are each a
quantity that depends on at least two image-sample differences,
each image-sample difference being a difference between a pair of
samples of image data, the samples being from a sequence of image
data samples across said any edge.
[0015] 2. The method of the above aspect, wherein the evaluating
the first measure does not depend upon detection of another
edge.
[0016] 3. The method of any one of the above aspects, wherein a 20%
decrease in an illumination of the scene does not result in a
difference whether the edge is omitted or allowed to contribute to
the focus signal.
[0017] 4. The method of any one of the above aspects, wherein the
determining determines whether the first value meets a
predetermined criterion that depends on at least the second value
in order to determine the relative extent.
[0018] 5. The method of Aspect 4, further comprising: omitting or
deemphasizing the edge in the generating the focus signal where the
edge does not meet the predetermined criterion.
[0019] 6. The method of any one of the above aspects, wherein the
determining determines the relative extent as a function of at
least the first and second measures.
[0020] 7. The method of Aspect 6, wherein the relative extent is a
weight for the edge in contributing to the generating the focus
signal.
[0021] 8. The method of any one of the above aspects, wherein any
edge that contributes to the focus signal contributes an
edge-sharpness measure that is a quantity that is computed from a
plurality of samples of image data within a predetermined
neighborhood of said any edge.
[0022] 9. The method of Aspect 8, wherein the edge-sharpness
measure is also the second measure.
[0023] 10. The method of Aspect 8, wherein the edge-sharpness
measure is neither the first measure nor the second measure.
[0024] 11. The method of Aspect 10, wherein the edge-sharpness
measure is not evaluated where the edge is omitted from the
generating of the focus signal.
[0025] 12. The method of Aspect 10, wherein the edge-sharpness
measure is a width of a predefined portion of the edge predefined
according to a predetermined manner.
[0026] 13. The method of Aspect 10, wherein the edge-sharpness
measure is a peak gradient value of the edge divided by a contrast
across the edge or across a predefined portion of the edge.
[0027] 14. The method of Aspect 10, wherein the edge-sharpness
measure is a second moment of gradients in the sequence of
gradients.
[0028] 15. The method of any one of the above aspects, wherein each
edge consists of a plurality of pixels arrayed contiguously in a
first direction and is detected by an edge detector.
[0029] 16. The method of Aspect 15, wherein the edge detector
detects said each edge using a first-order edge detection
operator.
[0030] 17. The method of any one of the above aspects, wherein the
first and second measures are mutually independent in the sense
that neither can be computed from the other without further
involving at least one sample of image data from a predetermined
neighborhood of the edge for which the first and second measures
are computed.
[0031] 18. The method of any one of the above aspects, wherein the
first and second measures and the edge-sharpness measure of any
edge are computed from a plurality of samples of image data within
a predetermined neighborhood of said any edge.
[0032] 19. The method of any one of the above aspects, wherein the
edge-sharpness measure of any edge has a unit of a power of a unit
of length, given that distance between gradients and count of
pixels both have a unit that is a unit of length, a gradient value
has a unit that is a unit of energy divided by a unit length, and
normalized gradient values are unitless.
[0033] 20. The method of any one of the above aspects, wherein the
edge-sharpness measure of any edge does not have a unit of energy
in its unit, given that each sample of image data has a unit that
is a unit of energy, that a difference between any pair of samples
of image data divided by a distance between the samples has a unit
that is a unit of energy divided by a unit of length, that distance
between a pair of gradients and count of pixels both have a unit
that is a unit of length, that gradient value has a unit that is a
unit of energy divided by a unit length and normalized gradient
values are unitless.
[0034] 21. The method of any one of the above aspects, wherein the
sharpness measure of the edge is not evaluated where the edge does
not contribute to the generating of the focus signal.
[0035] 22. The method of any one of the above aspects, wherein the
first and second measures are not affected by scaling the plurality
of samples of image data by a non-zero scaling factor while other
samples of image data are not scaled.
[0036] 23. The method of any one of the above aspects, wherein the
first and second measures are both affected by scaling the
plurality of samples of image data by a non-zero scaling
factor.
[0037] 24. The method of any one of the above aspects, wherein the
first measure is any one of: a width; a gradient value or a
difference between a pair of gradient values; a first derivative of
gradient; and an area under the gradients plotted against
distance.
[0038] 25. The method of any one of the above aspects, wherein the
first measure is any one of: a gradient value or a difference
between a pair of gradient values; a first derivative of gradient;
and an area under the gradients plotted against distance, each
being normalized by a peak gradient value or interpolated peak
gradient value of the edge.
[0039] 26. The method of any one of the above aspects, wherein the
first measure is an interpolated gradient value, normalized by a
peak gradient value or interpolated peak gradient value, at a
predefined distance from a peak gradient or interpolated peak of a
sequence of gradients across the edge.
[0040] 27. The method of any one of the above aspects, wherein the
first measure of any edge is a width of a predefined undivided
portion of said any edge, the predefined undivided portion being
defined in a predetermined manner with respect to the plurality of
samples of image data of said any edge.
[0041] 28. The method of any one of the above aspects, wherein the
second measure of any edge is a width of a predefined undivided
portion of said any edge, the predefined undivided portion being
defined in a predetermined manner with respect to the plurality of
samples of image data of said any edge.
[0042] 29. The method of any one of the above aspects, wherein the
edge-sharpness measure of any edge is a width of a predefined
undivided portion of said any edge, the predefined undivided
portion being defined in a predetermined manner with respect to the
plurality of samples of image data of said any edge.
[0043] 30. The method of Aspect 27 or 28 or 29, wherein the
predefined undivided portion is a narrowest undivided portion of
said any edge that contributes a predetermined fraction of a
contrast across said any edge.
[0044] 31. The method of Aspect 27 or 28 or 29, wherein the
predefined undivided portion consists of all pixels of said any
edge that have gradient values above a predetermined threshold.
[0045] 32. The method of Aspect 27 or 28 or 29, wherein the
predefined undivided portion is a widest undivided portion between
a positive peak (or interpolated peak) and a negative peak (or
interpolated peak) of a sequence of second order derivatives across
said any edge.
[0046] 33. The method of any one of the above aspects, wherein the
second measure of any edge is any one of: a width; a gradient value
or a difference between a pair of gradient values; a first
derivative of gradient; and an area under the gradients plotted
against distance.
[0047] 34. The method of any one of the above aspects, wherein the
second measure of any edge is any one of: a difference between
gradient values; and a first derivative of gradient, each being
normalized by a peak gradient value or interpolated peak gradient
value of said any edge and the second measure is not the
edge-sharpness measure of said any edge.
[0048] 35. The method of any one of the above aspects, wherein the
second measure of any edge is an interpolated gradient value,
normalized by a gradient value of a peak gradient or an
interpolated peak of a sequence of gradients across said any edge,
at a predefined distance from the peak gradient or the interpolated
peak and the second measure is not the edge-sharpness measure of
said any edge.
[0049] 36. The method of any one of the above aspects, wherein the
second measure of any edge measures a distance between a
gradient/interpolated gradient and another gradient/interpolated
gradient, both being part of a sequence of gradients across said
any edge and sharing a gradient value at a given percentage down
from a peak gradient or an interpolated peak of the sequence of
gradients.
[0050] 37. The method of any one of the above aspects, wherein the
second measure of any edge measures an area of a region under a
sequence of gradients of said any edge plotted against distance,
further normalized by a peak gradient value or an interpolated peak
gradient value of the sequence of gradients.
[0051] 38. The method of any one of the above aspects, wherein the
second measure of any edge is a peak gradient value of a sequence
of gradients across said any edge divided by a contrast across said
any edge.
[0052] 39. The method of any one of the above aspects, wherein the
second measure of any edge is a function of distances of a
plurality of gradients from a sequence of gradients across said any
edge from a predefined position relative to the plurality of
gradients.
[0053] 40. The method of Aspect 39, wherein the predefined position
is a center of gravity among the plurality of gradients, gradient
values being treated as weights.
[0054] 41. The method of Aspect 40, wherein the function measures a
k-th central moment of the plurality of gradients about the
predefined position, k being a positive even integer.
[0055] 42. The method of any one of the above aspects, wherein the
second measure of any edge is equal to a sum of a power of a
plurality of gradient values from a sequence of gradients across
said any edge, normalized by the power of a peak gradient value or
a interpolated peak gradient value interpolated for the sequence of
gradients, and the second measure is not the edge-sharpness measure
of said any edge.
[0056] 43. The method of Aspect 42, wherein each gradient value
among the plurality of gradient values either exceeds or is equal
to a predetermined fraction of the peak gradient value or the
interpolated peak gradient value.
[0057] 44. The method of Aspect 43, wherein for each gradient value
among the plurality of gradient values, a constant equal to the
predetermined fraction is subtracted from the second measure.
[0058] 45. The method of any one of the above aspects, wherein the
edge-sharpness measure of any edge measures a distance between a
gradient/interpolated gradient and another gradient/interpolated
gradient, both being part of a sequence of gradients across said
any edge and sharing a gradient value at a given percentage down
from a peak gradient or an interpolated peak of the sequence of
gradients.
[0059] 46. The method of any one of the above aspects, wherein the
edge-sharpness measure of any edge measures an area of a region
under a sequence of gradients of said any edge plotted against
distance, further divided by a peak gradient value or an
interpolated peak gradient value of the sequence of gradients.
[0060] 47. The method of any one of the above aspects, wherein the
edge-sharpness measure of any edge is a peak gradient value of a
sequence of gradients across said any edge divided by a contrast
across said any edge.
[0061] 48. The method of any one of the above aspects, wherein the
edge-sharpness measure of any edge is a function of distances of a
plurality of gradients of a sequence of gradients across said any
edge from a predefined position relative to the plurality of
gradients.
[0062] 49. The method of Aspect 48, wherein the predefined position
is a center of gravity among the plurality of gradients, gradient
values being treated as weights.
[0063] 50. The method of Aspect 48, wherein the function measures a
k-th central moment of the plurality of gradients about the
predefined position, k being a positive even integer.
[0064] 51. The method of Aspect 48, wherein each gradient value
among the plurality of gradient values either exceeds or is equal
to a predetermined fraction of the peak gradient value or the
interpolated peak gradient value.
[0065] 52. The method of Aspect 51, wherein for each gradient value
among the plurality of gradient values, a constant equal to the
predetermined fraction is subtracted from the second measure.
[0066] 53. The method of any one of the above aspects, further
comprising: storing the edge in a memory depending on the relative
extent.
[0067] 54. The method of any one of the above aspects, further
comprising: displaying the edge on a display depending on the
relative extent.
[0068] 55. The method of any one of the above aspects, wherein the
computing device comprises a buffer to store at least a portion of
the image at a time.
[0069] 56. The method of any one of the above aspects, wherein the
determining is not based on measuring an extent to which a sequence
of gradients across said each edge departs from perfect reflection
symmetry.
[0070] 57. The method of any one of the above aspects, wherein
there is a spurious sequence of gradients having perfect reflection
symmetry such that if the edge has the spurious sequence of
gradients across itself then the determining will reduce the
relative extent.
[0071] 58. The method of Aspect 57, wherein the spurious sequence
of gradients is {0, 0.2, 0.2, 0.7, 0.7, 1, 0.7, 0.7, 0.2, 0.2,
0}.
[0072] 59. The method of any one of the above aspects, wherein not
both the first and second measures of any edge involve measuring
widths or pixel counts at different gradient levels from a sequence
of gradients across said any edge.
[0073] 60. The method of any one of the above aspects, wherein,
where both the first and second measures are evaluated from a
sequence of gradients across the edge, not both the first and
second measures depend on gradients to both sides of a peak
gradient of the sequence of gradients.
[0074] 61. The method of any one of the above aspects, wherein
neither of the first and second measures is computed from one
positive gradient and one negative gradient.
[0075] 62. The method of any one of the above aspects, wherein the
first measure does not necessarily produce a value that satisfies
the criterion for every possible edge that has perfect reflection
symmetry in samples of image data in a predetermined neighborhood
of the edge about a line perpendicular to the edge and cutting
through a midpoint of the edge.
[0076] 63. A computer-readable medium that comprises
computer-executable instructions that, when executed by a computing
device, causes the computing device to execute a method according
to any one of the above method aspects.
[0077] 64. A circuit that generates a focus signal from a plurality
of edges of an image of a scene to indicate a degree of image
sharpness, comprising:
[0078] an edge detection and width measurement (EDWM) unit;
and,
[0079] a focus signal calculator,
[0080] wherein the edge detection and width measurement unit
detects edges in image data of the image, determines for the edges
the relative extents they will contribute respectively to the focus
signal, and evaluates edge-sharpness measures for edges that will
contribute to the focus signal,
[0081] wherein the focus signal calculator generates a focus signal
from the edge-sharpness measures, taking into account the
respective relative extents,
[0082] wherein the edge detection and width measurement (EDWM) unit
implements a method as described in any one of the above method
aspects.
[0083] 65. An image capture system, comprising:
[0084] a focus lens;
[0085] an aperture;
[0086] an image sensor comprising an image sensing pixel array;
[0087] a focus lens motor means; and,
[0088] a circuit according to Aspect 64.
BRIEF DESCRIPTION OF THE DRAWINGS
[0089] FIG. 1 is a schematic of an embodiment of an auto-focus
image pickup apparatus;
[0090] FIG. 2 is a schematic of an alternate embodiment of an
auto-focus image pickup apparatus;
[0091] FIG. 3 is a block diagram of a focus signal generator;
[0092] FIG. 4 is an illustration of a horizontal Sobel operator's
operation on a image signal matrix;
[0093] FIG. 5 illustrates a calculation of edge width from a
horizontal gradient;
[0094] FIG. 6A, 6B are illustrations of a calculation of an edge
width of a vertical edge having a slant angle .theta.;
[0095] FIG. 6C, 6D are illustrations of a calculation of an edge
width of a horizontal edge having a slant angle .theta.;
[0096] FIG. 7 is a flowchart of a process to calculate a slant
angle .theta. and correct an edge width for a vertical edge having
a slant;
[0097] FIG. 8 is an illustration of a vertical concatenated
edge;
[0098] FIG. 9A is an illustration of a group of closely-packed
vertical bars;
[0099] FIG. 9B is a graph of an image signal across FIG. 9A;
[0100] FIG. 9C is a graph of a horizontal Sobel gradient across
[0101] FIG. 9A;
[0102] FIG. 10 is a flowchart of a process to eliminate
closely-packed edges having shallow depths of modulation;
[0103] FIG. 11 is a histogram of edge widths illustrating a range
of edge widths for calculating a fine focus signal;
[0104] FIG. 12 is an illustration of a scene;
[0105] FIG. 13 is a graph illustrating a variation of a narrow-edge
count during a focus scan of the scene of FIG. 12;
[0106] FIG. 14 is a graph illustrating a variation of a gross focus
signal during a focus scan of the scene of FIG. 12;
[0107] FIG. 15 is a graph illustrating a variation of a fine focus
signal across a range of focus positions;
[0108] FIG. 16 is an illustration of an apparatus displaying
multiple objects in a scene and a selection mark over one of the
objects;
[0109] FIG. 17 is a block diagram of an alternate embodiment of a
focus signal generator;
[0110] FIG. 18 is a schematic of an alternate embodiment of an
auto-focus image pickup apparatus;
[0111] FIG. 19 is a schematic of an embodiment of an auto-focus
image pickup apparatus having a main pixel array and an auxiliary
pixel array;
[0112] FIG. 20 is a schematic of an alternate embodiment of an
auto-focus image pickup apparatus having a main pixel array and an
auxiliary pixel array;
[0113] FIG. 21 is a schematic of an alternate embodiment of an
auto-focus image pickup apparatus having a main pixel array and an
auxiliary pixel array;
[0114] FIG. 22 is an illustration of a variation of an edge width
from a main pixel array and a variation of an edge width from an
auxiliary pixel array at different focus positions;
[0115] FIG. 23A illustrates a symmetrical sequence of gradients of
an image signal across a good edge plotted against distance in
multiples of a spacing between successive gradients, and two widths
measured for two pairs of interpolated gradients, each pair at a
different gradient level;
[0116] FIG. 23B illustrates another symmetrical sequence of
gradients of an image signal across a spurious edge plotted against
distance in multiples of a spacing between successive gradients,
and two widths measured for two pairs of interpolated gradients,
each pair at a different gradient level, ratio of the smaller width
to the larger width being nearly double of that shown in FIG.
23A;
[0117] FIG. 24A illustrates a symmetrical sequence of gradients
across an edge plotted against distance in multiples of a spacing
between successive gradients, and a normalized gradient value of an
interpolated gradient at a predefined distance from a peak
gradient;
[0118] FIG. 24B illustrates a sequence of gradients across an edge
plotted against distance in multiples of a spacing between
successive gradients, and an area of a region under the plotted
sequence of gradients;
[0119] FIG. 24C illustrates a sequence of gradients of an image
signal across an edge plotted against distance in multiples of a
spacing between successive gradients, and a slope (i.e. second
derivative of the image signal) of the plotted sequence of
gradients taken at a gradient level defined with respect of an
interpolated peak gradient;
[0120] FIG. 24D illustrates a sequence of gradients of an image
signal across an edge plotted against distance in multiples of a
spacing between successive gradients, a center of gravity (i.e.
center of moment), and distances of the gradients from the center
of gravity;
[0121] FIG. 25 illustrates a sequence of second derivatives of an
image signal across an edge plotted against distance in multiples
of a spacing between successive second derivatives, showing (a) a
width W.sub.s between a pair of positive and negative peaks, (b) a
width W.sub.1 between a pair of outermost interpolated second
derivatives that have a given magnitude h.sub.1, (c) a width
W.sub.2 between an inner pair of interpolated second derivatives
that have the given magnitude h.sub.1, and (d) a distance D.sub.1
from a zero-crossing (between the pair of positive and negative
peaks) to an outermost interpolated second derivative that has the
given magnitude h.sub.1;
[0122] FIG. 26 illustrates a sequence of image data samples of the
image signal plotted against distance in multiples of a spacing
between successive samples, showing (a) a width W.sub.edge and a
contrast C.sub.edge between two samples at two ends of the edge,
(b) a peak gradient value a peak between a pair of samples that has
a steepest change of sample value, (c) an undivided portion of the
edge that has contrast C.sub.1 and width W.sub.part1, and (d) an
undivided portion of the edge that has contrast C.sub.2 and width
W.sub.part2;
[0123] FIG. 27A illustrates two symmetrical sequences of gradients
plotted against distance in multiples of a spacing between
successive samples of each sequence, the sequences normalized with
respect to their respect peak gradients, where the plot for one
sequence has a triangular shape and the plot for the other sequence
has a shape of a hat;
[0124] FIG. 27B illustrates two symmetrical sequences of gradients
plotted against distance in multiples of a spacing between
successive samples of each sequence, the sequences normalized with
respect to their respect peak gradients, where the plot for one
sequence has a triangular shape down to a normalized gradient level
and the plot for the other sequence has a shape of a dome;
[0125] FIG. 28 shows a scatter plot of four pairs of expected
values of first and second shape measures (w.sub.1b, w.sub.1a),
(w.sub.2b, w.sub.2a), (W.sub.3b, W.sub.3a), (w.sub.4b, W.sub.4a),
and illustrates a value w'.sub.b for the first shape measure is
found by interpolation from a value w'.sub.a for the second shape
measure;
[0126] FIG. 29 illustrates finding an interpolated peak's position
by interpolation;
[0127] FIG. 30 shows an alternate embodiment of a focus signal
generator.
DETAILED DESCRIPTION
[0128] Disclosed is an auto focus image system that includes a
pixel array coupled to a focus signal generator. The pixel array
captures an image that has at least one edge with a width. The
focus signal generator may generate a focus signal that is a
function of the edge width and/or statistics of edge widths. An
auto focus image system that includes a pixel array coupled to a
focus signal generator. The pixel array captures an image that has
at least one edge with a width. The generator generates a focus
signal that is a function of the edge width and various statistics
of edge width. The generator may eliminate an edge having an
asymmetry of a gradient of an image signal. The generator may also
eliminate an edge that fails a template for an associated peaking
in the gradient. A processor receives the focus signal and/or the
statistics of edge widths and adjusts a focus position of a focus
lens. The edge width can be determined by various techniques
including the use of gradients. A histogram of edge widths may be
used to determine whether a particular image is focused or
unfocused. A histogram with a large population of thin edge widths
is indicative of a focused image.
Architecture
[0129] Referring to the drawings more particularly by reference
numbers, FIG. 1 shows an embodiment of an auto-focus image capture
system 102. The system 102 may be part of a digital still camera,
but it is to be understood that the system can be embodied in any
device that requires controlled focusing of an image. The system
102 may include a focus lens 104, a pixel array and circuits 108,
an A/D converter 110, a processor 112, a display 114, a memory card
116 and a drive motor/circuit 118. Light from a scene enters
through the lens 104. The pixel array and circuits 108 generates an
analog signal that is converted to a digital signal by the A/D
Converter 110. The pixel array 108 may incorporate a mosaic color
pattern, e.g. the Bayer pattern. The digital signal may be sent to
the processor 112 that performs various processes, e.g. color
interpolation, focus position control, color correction, image
compression/decompression, user interface control, and display
control, and to the focus signal generator 120. Where the focus
signal generator 120 and the processor 112 reside within different
packages, a color interpolation unit 148 may be implemented to
perform color interpolation on the digital signal 130 to estimate
the missing color signals on each pixel for the focus signal
generator 120. Alternately, where the focus signal generator 120
and the processor 112 reside together within a package 144, the
focus signal generator 120 may input interpolated color images from
the processor 112 on bus 146 as shown in FIG. 2 or a single image
signal derived from the original image signal generated from the
A/D converter 110, for example a grayscale signal.
[0130] The focus signal generator 120 receives a group of control
signals 132 from the processor 112, in addition, and may output
signals 134 to the processor 112. The output signals 134 may
comprise one or more of the following: a focus signal 134, a
narrow-edge count, and a set of numbers representing a statistics
of edge width in the image. The processor 112 may generate a focus
control signal 136 that is sent to the drive motor/circuit 118 to
control the focus lens 104. A focused image is ultimately provided
to the display 114 and/or stored in the memory card 116. The
algorithm(s) used to adjust a focus position may be performed by
the processor 112.
[0131] The pixel array and circuits 108, A/D Converter 110, focus
signal generator 120, and processor 112 may all reside within a
package. Alternately, the pixel array and circuits 108, A/D
Converter 110, and focus signal generator 120 may reside within a
package 142 as image sensor 150 shown in FIG. 1, separate from the
processor 112. Alternately, the focus signal generator 120 and
processor 112 may together reside within a package 144 as a camera
controller 160 shown in FIG. 2, separate from the pixel array 108
and A/D Converter 110. The focus signal generator 120 (or any
alternative embodiment, such as one shown in FIG. 30) and the
processor 112 may together reside on a semiconductor substrate,
such as a silicon substrate.
Focus Signal Generator
[0132] FIG. 3 shows an embodiment of a focus signal generator 120
receiving image(s) from a image providing unit 202. The image
providing unit 202 may be the color interpolator 148 in FIG. 1 or
the processor 212 in FIG. 2. The focus signal generator 120 may
comprise an edge detection & width measurement (EDWM) unit 206,
a focus signal calculator 210, a length filter 212, and a width
filter 209. It may further comprise a fine switch 220 controlled by
input `fine` 222. The focus signal generator 120 may provide a
narrow-edge count from the width filter 209 and a focus signal from
the focus signal calculator 210, the focus signal being
configurable between a fine focus signal and a gross focus signal,
selectable by input `fine` 222. Alternately, both fine focus signal
and gross focus signal may be calculated and output as part of
output signals 134. The edge detection & width measurement unit
206 receives image(s) provided by the image providing unit 202. In
the context of FIGS. 1 and 2, control signals, such as control
signal `fine` 222, may be provided by the processor 112 in signals
132. Also in the context of FIGS. 1 and 2, the output signals 134
may be provided to the processor 112, which functions as a focus
system controller that controls the focus position of the focus
lens 104 to bring images of objects into sharp focus on the pixel
array 108 by analyzing the output signals 134 to detect a sharp
object in the image. Various components of the focus signal
generator 120 are described below.
[0133] The EDWM unit 206 may transform the input image such that
the three signals of the image, red (R), green (G) and blue (B) are
converted to a single image signal. Several techniques can be
utilized to transform an image to a single image. RGB values can be
used to calculate a luminance or chrominance value or a specific
ratio of RGB values can be taken to form the single image signal.
For example, the luminance value can be calculated with the
equation Y=0.2126*R+0.7152*G+0.0722*B, where Y is luminance value.
The single image signal may then be processed by a Gaussian filter
or any lowpass filter to smooth out image data sample values among
neighboring pixels to remove a noise.
[0134] The focus signal generator 120, 120', 120'' is not limited
to grayscale signal. It may operate on any one image signal to
detect one or more edges in the image signal. Or it may operate on
any combination of the image signals, for example Y, R-G, or B-G.
It may operate on each and every one of the R, G, B image signals
separately, or any one or more combinations thereof, to detect
edges. It may form statistics of edge widths for each of the R, G,
B image signals, or any combination thereof. It may form a focus
signal from statistics of edge widths from one or more image
signals.
[0135] The focus signal generator includes an edge detector to
identify an edge in an image signal. The edge detector may use a
first-order edge detection operator, such as Sobel operator,
Prewitt operator, Roberts Cross operator, or
[0136] Roberts operator. The edge detector may use a higher-order
edge detection operator to identify the edge, for example a second
order operator such as a Laplacian operator. The edge detector may
use any one of the known edge detection operators or any improved
operator that shares a common edge detection principle of any of
the known operators.
[0137] Where the edge detector uses a first-order edge detection
operator, a gradient (i.e. first derivative) of the image signal is
computed. There are various methods available to calculate the
gradient, including using any one of various first order edge
detection operators such the Sobel operator, the Prewitt operator,
the Roberts Cross operator, and the Roberts operator. The Roberts
operator has two kernels which are single column or single row
matrices: [-1 +1] and its transpose. The Roberts Cross operator has
two kernels which are 2-by-2 matrices: [+1, 0; 0, -1] and [0, +1;
-1, 0], shown in the format of [<first-row vector; second-row
vector; third-row vector] like in Matlab. The Prewitt and the Sobel
operator are basically have the same kernels, [-1, 0, +1] taking
gradient in a direction of the row and its transpose taking
gradient in a direction of the column, further multiplied by
different lowpass filter kernels performing lowpass filterings
perpendicular to the respective gradient directions. Gradients
across the columns and the rows may be calculated to detect
vertical and horizontal edges respectively, for example using a
Sobel-X operator and a Sobel-Y operator, respectively. Sobel
X-operator at pixel location [k, q] where k is a row number and q
is a column number, is given by the equation Sx[k, q]=U[k,
q+1]-U[k, q-1]. Sobel Y-operator at the same location is given by
the equation Sy[k,q]=U[k+1,q]-U[k-1,q], where U is an image signal
of the processed image.
[0138] Where the edge detector uses a second-order operator, a
second derivative (such as the Laplacian) of the image signal is
computed.
Orientation Tagging
[0139] Each pixel may be tagged either a horizontal edge (`H`) or a
vertical edge (`V`) if either vertical or horizontal gradient
magnitude exceeds a predetermined lower limit ("elimination
threshold"), e.g. 5 for an 8-bit image, or no edge if neither is
true. This lower limit eliminates spurious edges due to gentle
shading or noise. A pixel may be tagged a vertical edge if its
horizontal gradient magnitude exceeds its vertical gradient
magnitude by a predetermined hysteresis amount or more, e.g. 2 for
an 8-bit image, and vice versa. If both gradient magnitudes differ
less than the hysteresis amount, the pixel gets a direction tag
same as that of its nearest neighbor that has a direction tag
already determined. For example, if the image is scanned from left
to right in each row and from row to row downwards, a sequence of
inspection of neighboring pixels may be the pixel above first, the
pixel above left second, and the pixel on the left third, and the
pixel above right last. Applying this hysteresis helps to ensure
that adjacent pixels get similar tags if each of them has nearly
identical horizontal and vertical gradient magnitudes. FIG. 4
illustrates the result of tagging on a 6-by-6 array of horizontal
and vertical gradients. In each cell, the horizontal gradient is in
the upper-left, vertical gradient is on the right, and direction
tag is at the bottom. Only pixels that have either horizontal or
vertical gradient magnitude exceeding 5 qualify at this step as
edge pixels are printed in bold and get direction tags.
[0140] The image, gradients and tags may be scanned horizontally
for vertical edges, and vertically for horizontal edges. Each group
of consecutive pixels in a same row, having a same horizontal
gradient polarity and all tagged for vertical edge may be
designated a vertical edge if no adjacent pixel on left or right of
the group are likewise. Likewise, each group of consecutive pixels
in a same column having a same vertical gradient polarity and all
tagged for horizontal edge may be designated a horizontal edge if
no adjacent pixel above or below the group satisfies the same. Thus
horizontal and vertical edges may be identified.
Edge Width
[0141] Each edge may be refined by removing pixels whose gradient
magnitudes are less than a given fraction of the peak gradient
magnitude within the edge. FIG. 5 illustrates this step using a
refinement threshold equal to one third of the edge's peak gradient
magnitude, refining the edge width down to 3 from the original 9.
This edge refinement distinguishes the dominant gradient component
that sets the apparent edge width that dominates visual perception
of the edge's sharpness despite an image having multiple
overlapping shadings that may cause gradients to gently decay over
many pixels.
[0142] Edge width may be calculated in any one of known methods.
One method of calculating edge width is simply counting the number
of pixels within an edge. An alternate method of calculating edge
width is shown in FIG. 5. In
[0143] FIG. 5, a first fractional pixel position (2.4) is found
between a first outer pixel (pixel 3) of a refined edge and the
adjacent outside pixel (pixel 2) by an interpolation from the
refinement threshold 304. Likewise, a second fractional pixel
position (5.5) is found between a second outer pixel (pixel 5) and
its adjacent outside pixel (pixel 6). The edge width is found as
the difference between these two fractional pixel positions,
5.5-2.4=3.1.
[0144] Another alternative edge width calculation method is to
calculate a difference of the image signal across the edge (with or
without edge refinement) and divide it by a peak gradient of the
edge.
[0145] Alternatively, edge width may be a distance between a pair
of positive and negative peaks (or interpolated peak(s)) of the
second order derivative of the image signal across the edge. Other
alternatives are possible, to be described under the heading
"edge-sharpness measure" further into this specification.
[0146] It will be seen further into this specification under the
heading "edge-sharpness measure" that there are other alternatives
than a width, which is merely one example of a edge-sharpness
measure that is essentially independent of illumination of the
scene.
Slant Correction
[0147] Although each edge may be assigned to one prescribed
direction (e.g. vertical direction or horizontal direction) or
another, perpendicular, prescribed direction (e.g horizontal
direction or vertical direction) and may have its edge width
measured in a direction perpendicular to that assigned edge
direction, the boundaries between regions of different image signal
values in the image from which these edges arise may not be and
usually are not aligned perfectly with either prescribed
directions. In FIG. 6A, a boundary (shaded band) is shown to be
inclined at a slant angle .theta. with respect to the vertical
dashed line, and a width a is shown to be measured in the
perpendicular direction (i.e. horizontal direction). However, a
width b (as indicated in the drawing) measured in a direction
perpendicular to the direction of the boundary (also direction of
an edge that forms a part of the boundary) is more appropriate as
the width of the boundary (and also of the edge) than width a. Such
widths a that are not measured perpendicularly to the respective
edge directions tend to be too large and do not represent the
genuine thickness of the respective boundaries.
[0148] For purposes of calculating a focus signal from edge widths,
the edge widths measured in one or the other of those prescribed
directions are to be corrected by reducing them down to be widths
in directions perpendicular to directions of the respective edges.
The Edge Detection and Width Measurement Unit 206 performs such a
correction on edge widths. As shown in FIG. 6A, the measured width
a is the length of the hypotenuse of a right-angled triangle that
has its base (marked with width b) straddling across the shaded
boundary perpendicularly (thus perpendicular to the edge direction)
and that has the angle .theta.. The corrected width b may then be
obtained from a projection of the measured width a to the direction
perpendicular to the edge direction. From elementary trigonometry,
such a projection may be given by b=a cos (.phi.), but
approximation may be used as long as it obtains accuracy to within
20%. The angle .theta., or cos (.phi.) itself, may be found by any
method known in the art for finding a direction of an edge in an
image, or by a more accurate method described in the flowchart
shown in FIG. 7.
[0149] Each horizontal or vertical edge's edge width may be
corrected for its slant from either the horizontal or vertical
orientation (the prescribed directions), respectively. FIG. 6A, 6B
illustrate a correction calculation for an edge width measured in
the horizontal direction for a boundary (and hence edges that form
the boundary) that has a slant from the vertical line. FIG. 6C, 6D
illustrate a correction calculation for an edge width measured in
the vertical direction for a boundary (and hence edges that form
the boundary) that has a slant from the horizontal line. The
correction may be made by multiplying the edge width measured in a
prescribed direction, such as a vertical direction or a horizontal
direction, by a factor of cos .phi., where .phi. is an angle of
slant from the prescribed direction.
[0150] By way of example, FIG. 7 shows a flowchart of a process to
correct edge widths for slant for edges inclined from a vertical
line. (For horizontal edges, substitute `row` for `column`, and
interchange `vertical` with `horizontal` in the flowchart.)
[0151] From step 502 to step 506, a slant angle .theta. is found.
For each vertical edge, at step 502, locate the column position
where the horizontal gradient magnitude peaks, and find the
horizontal gradient x. At step 504, find where the vertical
gradient magnitude peaks along the column position and within two
pixels away, and find the vertical gradient y.
[0152] At step 506, find the slant angle .theta.=tan .sup.-1 (y/x).
At step 506, the slant angle may be found by looking up a lookup
table. Although steps 502 to 506 present one specific procedure and
method to find the slant angle, other procedures and methods known
in the art may be used instead.
[0153] Finally, at step 508, scale down the edge width by
multiplying with cos (.phi.), or with an approximation thereto as
one skilled in the art usually does in practice.
[0154] A first modification of the process shown in FIG. 7 is to
substitute for step 506 and part of step 508 by providing a lookup
table that has entries for various combinations of input values of
x and y. For each combination of input values of x and y, the
lookup table returns an edge width correction factor. The edge
width correction factor output by the lookup table may be an
approximation to cos (tan.sup.-1 (y/x)) to within 20%, preferably
within 5%. The edge width is then multiplied with this correction
factor to produce a slant-corrected edge width.
[0155] A second modification is to calculate a quotient y/x between
a vertical gradient y and a horizontal gradient x to produce a
quotient q, then use q to input to a lookup table that has entries
for various values of q. For each value of q, the lookup table
returns an edge width correction factor. The edge width correction
factor may be an approximation to cos (tan .sup.-1 (q)) to within
20%, preferably within 5%.
[0156] For finding the slant angle .theta. (or an approximation
thereto such that the correction factor is accurate to within 20%)
and subsequently the correction factor cos (.phi.) (or an
approximation thereto), or to directly find the correction factor
without finding the slant angle .theta. (as in the first and second
modifications), the values of x and y may be obtained in steps 502
to 506, but other methods may be employed instead.
[0157] A third modification is to perform the following for each
one of a plurality of pixels in the edge: (a) find horizontal
gradient x and vertical gradient y both for a pixel, (b) find q=y/x
for this pixel, and (c) find a correction factor that corresponds
to q, for instance cos (tan .sup.-1 (q)) or an approximation
thereto to within 20%. Finally, find the correction factor for the
edge width by averaging across the correction factor from each of
the plurality of pixels. The average may be a weighted average,
such as one in which a pixel that has a larger horizontal gradient
is given a larger weight than another pixel that has a lesser
horizontal gradient.
[0158] Other modifications are possible along these directions or
other.
Screen Threshold
[0159] Adjacent edges may be prevented altogether from contributing
to a focus signal, or have their contributions attenuated, if their
peak gradient magnitudes are below a predetermined fraction of an
adjacent wider edge's peak gradient magnitude. FIG. 9A, 9B, and 9C
illustrate a problem that is being addressed.
[0160] FIG. 9A illustrates three vertical white bars separated by
two narrow black spaces each 2 pixels wide. The middle white bar is
a narrow bar 2 pixels wide. FIG. 9B shows an image signal plotted
horizontally across the image in FIG. 9A for each of a sharp image
and a blurred image. FIG. 9C plots Sobel-x gradients of FIG. 9B for
the sharp image and blurred image. In FIG. 9C, the first edge
(pixels 2-5) for the blurred image is wider than that for the sharp
image, and likewise the last edge (pixels 13-15) as expected.
However, the two narrowest edges (pixels 9 & 10, and pixels 11
& 12) have widths of two in both images. In FIG. 9B, the
corresponding slopes at pixels 9 & 10, and pixels 11 & 12,
each takes two pixels to complete a transition. The blurred image,
however, has a significant decline of peak gradient magnitude, as
much as 50%, from the wider edge to the narrower edges. The sharp
image, on the other hand, changes less than 10% between the wider
and the narrower edges.
[0161] The significant decline, e.g. 20% or greater, in peak
gradient magnitude for a narrower edge adjacent to a wider edge
having an opposite-signed gradient gives a hint that the blurred
image is not well focused, and thus the narrower edge should not be
relied upon as an indication that the blurred image is sharp.
[0162] Likewise, mutually adjacent edges of alternating gradient
polarities should not be relied upon for such indication even if
their edge width are small as long as they are in close proximity
to each other, e.g. no more than 1 pixel apart ("minimum edge
gap"). The minimum edge gap is in terms of a number of pixels, e.g.
1, or 2, or in between.
[0163] Furthermore, given that one edge may have been eliminated
due to having a peak gradient less than the elimination threshold,
two successive edges having an identical gradient polarity and
spaced no more than two times the minimum edge gap plus a
sharp_edge_width (sharp_edge_width is a number assigned to
designate an edge width of a sharp edge) apart may be used as a
condition for eliminating or demoting a contribution from one or
both of the two mutually adjacent edges. either.
[0164] The Edge Detection and Width Measurement Unit 206 may
execute the following algorithm for eliminating closely-packed
narrower edges based on a screen threshold established from a wider
edge , and a modulation screen flag that can be turned on and
off.
[0165] For each edge, the screen threshold and screen flag to be
used for the immediate next edge of an opposite polarity are
determined according to the process of the flowchart shown in FIG.
10.
[0166] Given the screen threshold and screen flag, an edge may be
eliminated unless one of the following conditions is true: (a) the
screen flag is off for this edge, (b) a peak gradient magnitude of
the edge is not smaller than the screen threshold for this edge. To
conditions (a) and (b) may be added condition (c) the edge width is
not less than sharp_edge_width+1, where a number has been assigned
for sharp_edge_width to designate an edge width of a sharp edge,
and where the "+1" may be varied to set a range of edge widths
above the sharp_edge_width within which edges may be eliminated if
they fail (a) and (b). For the example shown in FIGS. 9A-9C,
sharp_edge_width may be 2. FIG. 10 is a flowchart to determine a
screen threshold and a screen flag for each edge. For vertical
edges, assume scanning from left to right along a row, though this
is not required. (For horizontal edges, assume scanning from top to
bottom along a column, though this is not required.) A number is
assigned for sharp_edge_width and may be 2 for the example shown in
FIGS. 9A-9C. Starting at the first edge at step 702, each edge is
queried at step 720 as to whether its edge width is greater than or
equal to one plus sharp_edge_width, the value of one being the
minimum edge gap value used for this illustration, but a different
value may be used, such as between 0.5 and 2.0. If yes, the edge is
a wider edge, and step 706 follows to set the screen threshold for
the immediate next edge that has an opposite polarity to beta times
a peak gradient magnitude of the edge, beta being from 0.3 to 0.7,
preferably 0.55, then step 708 follows to turn on the screen flag
for the next edge, then proceed to the next edge. If no, the edge
is not a wider edge, and step 730 follows to check whether the
spacing from the prior edge of the same gradient polarity is
greater than two times the minimum edge gap (or a different
predetermined number) plus sharp_edge_width and the immediate prior
edge of an opposite polarity, if any, is more than the minimum edge
gap away. If yes, step 710 follows to turn off the screen flag for
the next edge. If no, keep the screen flag and the screen threshold
for the next edge and proceed to the next edge. Beta may be a
predetermined fraction, or it may be a fraction calculated
following a predetermined formula, such as a function of an edge
width. In the latter case, beta may vary from one part of the image
to another part.
ALTERNATIVE EMBODIMENTS
Orientation of the Pixel Grid:
[0167] The image input by the focus signal generator 120 may have
pixels laid out in a rectangular grid ("pixel grid") rotated at 45
degrees with respect to a rectangular frame of the image. In this
case, the X- and Y-directions of the edge detection operations and
width measurement operations may be rotated likewise.
Edge-sharpness Measures:
[0168] In the above description, sharpness of image of an edge is
represented by a width of the edge measured from a sequence of
gradients across the edge with the gradients oriented across the
edge, there are alternatives that work on similar principle. In
essence, what allows the focus signal generated in this manner is
that the individual edges contributes a quantity (hereinafter
"edge-sharpness measure") that is independent of scaling the image
data by, for example, 20%, or essentially independent, such as
changes by not more 5% for 20% scaling down of the image data, thus
helping to make the focus signal independent of or far less
dependent on illumination of the scene of the image or reflectivity
of objects in the scene compared with the conventional contrast
detection method.
[0169] In the present focus signal generator 120, any
edge-sharpness measure that has the above characteristic of being
independent of or essentially independent of 20% scaling down of
the image data in addition is a good alternative to the width
measured from a gradient or interpolated gradient to another
gradient or interpolated gradient of a same gradient value.
[0170] The alternative edge-sharpness measure preferably has a unit
that does not include a unit of energy. The unit of the
edge-sharpness measure is determined on basis two points: (a) each
sample of the image data on which the first-order edge-detection
operator operates on has a unit of energy, (b) distance between
samples has a unit of length. On basis of points (a) and (b), a
gradient value has a unit of a unit of energy divided by a unit of
length. Likewise, contrast across the edge or across any undivided
portion of the edge has a unit of energy. Therefore the contrast is
not a good edge-sharpness measure, as the unit reveals that it is
affected by illumination of the scene and reflectivity of the
object. Neither is peak gradient of the edge, because the unit of
the peak gradient has a unit of energy in it, indicating also that
it is responsive to a change in illumination of the scene. On the
other hand, peak gradient of the edge divided by a contrast of the
edge is a good edge-sharpness measure, as it has a unit of the
reciprocal of a unit of length. As another example, the count of
gradients whose gradient values exceeds a certain predetermine
fraction of the peak gradient is a good edge-sharpness measure, as
the count is simply a measure of distance quantized to the size of
the spacing between contiguous gradients, hence having a unit of
length.
[0171] It is here noted that, in the generation of the
edge-sharpness measure, a gradient may be generated from a
first-order edge detection operator used to detect the edge, or may
be generated from a different first-derivative operator (i.e.
gradient operator). For example, while the Sobel operator (or even
a second-order edge detection operator, such as a Laplacian
operator) may be used to detect the edge, the Roberts operator
whose kernels are simply [-1, +1] and its transpose, which is
simply subtracting one sample of the image data from the next
sample in the orientation of the gradient operator, with the
resulting gradient located midway between the two samples. Edges
may be detected with a higher-order edge detection operator than
first-order independently of one or more derivative operators used
in generating the edge-sharpness measure or any of the shape
measures described in the next section.
[0172] Viewing it another way, the edge-sharpness measure should
have a unit of a power of a unit of length, for example a square of
a unit of length, a reciprocal of a unit of length, the unit of
length itself, or a square-root of a unit of length.
[0173] Any such alternative edge-sharpness measure can replace the
edge width in the focus signal generator 120.
[0174] To correct for a slant of the edge, the correction factor as
described above with reference to FIGS. 6A-6D and FIG. 7
(hereinafter "width correction factor") should be converted to
adopt the same power. For example, if the edge-sharpness measure is
peak gradient divided by a contrast, which gives it a unit of the
reciprocal of a unit of length, then the appropriate correction
factor for the edge-sharpness measure is the reciprocal of the
correction factor described with reference to FIGS. 6A-6D and FIG.
7 above. As another example, if the edge-sharpness measure has a
unit of a square of a unit of length, then the slant correction
factor for the edge-sharpness measure should be a square of the
width correction factor.
[0175] Several examples of alternative edge-sharpness measures are
described below with reference to the drawings in FIG. 24B, FIG.
24D, FIG. 25, and FIG. 26.
[0176] FIG. 24B illustrates a sequence of gradients across an edge
plotted against distance in multiples of a spacing between
successive gradients, and an area A.sub.3 of a shaded region under
the plotted sequence of gradients. In this example, the region is
defined between two gradient levels L.sub.1 and L.sub.2, which may
be defined with respect to an interpolated peak gradient value
(alternatively, the peak gradient value) of the sequence of
gradients as, for example, predetermined portion of the
interpolated peak gradient value. The shaded region has four
corners of interpolated gradients. The area divided by the
interpolated peak gradient value (alternatively, the peak gradient
value) is a good edge-sharpness measure, as it has a unit of
length. It is noted that alternative definitions of the region are
possible. For example, the region may be bounded from above not by
the gradient level L.sub.1 but by the sequence of gradients.
[0177] FIG. 24D illustrates a sequence of gradients of samples of
the image data across an edge plotted against distance in multiples
of a spacing between successive gradients, a center of gravity 3401
(i.e. center of moment), and distances u.sub.2, U.sub.3, U.sub.4,
u.sub.5 and u.sub.6 of the gradients (having gradient values
g.sub.2, g.sub.3, g.sub.4, g.sub.5 and g.sub.6) from the center of
gravity. A good edge-sharpness measure is a k-th central moment of
the gradients about the center of gravity, namely a weighted
average of the distances of the gradients from the center of
gravity with the weights being magnitudes of the respective
gradients, k being an even integer. For example, k can be 2, which
makes the edge-sharpness measure a variance as if the sequence of
gradients were a probability distribution. In this example, the
edge-sharpness measure has a unit of a square of a unit of length.
More generally, the edge-sharpness measure may be a function of
distances of a plurality of gradients of a sequence of gradients
from a position predefined relative to the plurality of gradients,
the sequence being array across the edge. Other than the center of
gravity, the predefined position may be an interpolated peak
position for the sequence of gradients. A proper subset of the
gradients of edge may be chosen according to a predefined criterion
to participate in this calculation. For example, the gradients may
be required to have gradient values at least a predetermined
fraction of the peak gradient or gradient value of an interpolated
peak of the sequence of gradients.
[0178] FIG. 25 illustrates a sequence of second derivatives of a
sequence of samples of image data across an edge plotted against
distance in multiples of a spacing between successive second
derivatives, showing (a) a width W.sub.8 between a pair of positive
and negative peaks, (b) a width W.sub.1 between a pair of outermost
interpolated second derivatives that have a given magnitude
h.sub.1, (c) a width W.sub.2 between an inner pair of interpolated
second derivatives that have the given magnitude h.sub.1, and (d) a
distance D.sub.1 from a zero-crossing (between the pair of positive
and negative peaks) to an outermost interpolated second derivative
that has the given magnitude h.sub.1. Any one of the three widths
W.sub.s, W.sub.1 and W.sub.2 may used as the edge-sharpness
measure.
[0179] In the example of FIG. 25, furthermore, the edge-sharpness
measure may be a weighted sum of distances from the zero-crossing
(between the pair of positive and negative peaks, and may be
interpolated) of the second derivatives with the weights being
magnitudes of the respective second derivatives. More generally,
the edge-sharpness measure may be a function of distances of a
plurality of second derivatives across the edge from a predefined
position relative to the plurality of second derivatives. Other the
zero-crossing position, a center of gravity is a good candidate for
the predefined position, with the weights being magnitudes of the
second derivatives. Yet another good candidate for the predefined
position may be the midway point between the pair of positive and
negative gradients.
[0180] FIG. 26 illustrates a sequence of samples of image data from
pixels of an edge plotted against distance in multiples of a
spacing between contiguous pixels, showing (a) a width W.sub.edge
and a contrast C.sub.edge between two samples at two ends of the
edge, (b) a peak gradient value g.sub.peak (generated by the
Roberts operator) between a pair of samples that has a steepest
change of sample value, (c) a narrowest undivided portion of the
edge that has contrast C.sub.1 and width W.sub.part1, and (d) a
narrowest undivided portion of the edge that has contrast C.sub.2
and width W.sub.part2. As mentioned before, the peak gradient value
g.sub.peak divided by the contrast C.sub.edge is a good
edge-sharpness measure. The width W.sub.edge is another good
edge-sharpness measure. The widths W.sub.part1 and W.sub.part2 are
also good alternatives. The contrasts C.sub.1 and/or C.sub.2 may be
defined to be a predetermine portion of the edge contrast
C.sub.edge. I Alternatively, any one of them may be defined to be a
predetermined multiple of a peak gradient of the edge, such as the
peak gradient g.sub.peak. It is also noted here that the "narrowest
undivided portion" may be delimited by interpolated samples of
image data, such as shown in squares in FIG. 26, or by rounding
down or up to a nearest pixel count.
Edge Qualification
[0181] FIGS. 23A and 23B show a pair of symmetrical sequences of
gradients of the image signal across a two different edges, plotted
against distance in multiples of a spacing between successive
gradients. In each figure, two widths (an upper width and a lower
width) are measured for two pairs of interpolated gradients, each
pair at a different gradient level.
[0182] It can be seen that, while the lower widths of the two
sequences of gradients differ by merely 10%, the upper width in
FIG. 23B is nearly double the upper width in FIG. 23A. Not both of
them can be accepted as valid edges for deriving a reliable focus
signal from.
[0183] It is recognized by this inventor that merely a single
measurement made from samples of image data at and around the edge
is insufficient to distinguish a spurious edge even though the
spurious shape may have perfect reflection symmetry in gradients
across the itself. This inventor recognizes that at least two
measurements made from samples of the image data within a
predefined neighborhood of the edge are necessary to determine that
an edge should have be de-emphasized or omitted altogether from
contributing to the focus signal.
[0184] The EDWM unit implements such a method to qualify edges for
participation in generating the focus signal. The method is based
on taking at least two measurements made from samples of the image
data within a predetermined neighborhood of the edge (hereinafter
"shape measures"). The predetermined neighborhood may be all the
image data samples from which all gradients and/or second
derivatives within the edge are computed from for detection of the
edge. Alternatively, the predetermined neighborhood may be all
pixels within a predetermined distance of the edge, for example 8
pixels, or a minimal distance sufficient to include all image data
samples used for detecting the edge and/or computing the
edge-sharpness measure of the edge.
[0185] Each shape measure of the edge is measured from at least two
sample-pair differences, where each sample-pair difference is a
difference between a pair of samples of image data, these samples
being from a sequence of samples of image data arrayed across the
edge.
[0186] The method then may determine to reduce a relative extent to
which the edge contributes to the focus signal (as compared with
other edges that contribute to the focus signal) depending on
values of at least two shape measures for the edge. For example,
where the focus signal is computed as a weighted average of all
edges that are allowed to contribute, the weights having been
already determined through other methods (such as the length filter
described in the next section), the weight of the edge may be
further reduced as compared to other edges by multiplying with a
factor (hereinafter "shape-qualifying factor") computed from the
determining as the relative extent.
[0187] The method may determine whether together the at least two
shape measures meet a criterion in order to determine the relative
extent to reduce the edge's contribution to the focus signal. For
example, the criterion may be expressed as a boundary separating a
region of no or little reduction in the relative extent from all
other region(s) in a two-dimensional scatter plot of the first
measure against the second measure. Contours may be defined such
that pairs of first-measure value and second-measure value that
will be assign same relative extent are on same contour, and the
relative extent is read from a memory by looking up to which
contour the pair for the edge belongs. For example, the method may
evaluate whether one of the at least two shape measures meets a
criterion that depends on one or more of the other shape measures.
For example, the criterion may require that a first shape measure
is within a predetermined tolerance of an expected value, which is
a function of the second shape measure. Following from the
evaluating, the edge may be omitted or de-emphasized in generating
the focus signal where the criterion is not satisfied. For example,
where the edge is to be de-emphasized, the relative extent may be a
function that varies between one value (e.g. one) for satisfying
the criterion to another value for not satisfying the criterion
(e.g. zero) and having a smooth transition with respect to
variation of the difference between the value of the first measure
and the expected value, and the relative extent can be used to
reduce a weight of the edge in the focus signal by multiplying the
weight with this relative extent prior to calculating the focus
signal, where the focus signal is a weighted average from edges
that contribute to it.
[0188] Such function preferably assumes a shape of a sigmoid
function with respect to the difference.
[0189] Alternatively, the method may compute the relative extent as
a function of the at least two shape measures. For example, the
relative extent may be computed as X/E, where E is the expected
value (found from plugging the measured value for the edge for the
second measure into the function) and X is the measured value for
the first shape measure.
[0190] The expected value of the first measure in terms of the
second measure may be expressed in a mathematical formula recorded
in a computer-readable medium, such as a non-volatile memory (for
example flash memory), and retrieved into the EDWM unit for
execution. Alternatively, a lookup table stored in the
computer-readable medium can be used. Referring to FIG. 28 shows a
scatter plot of four pairs of values of first and second shape
measures (w.sub.1b, w.sub.1a), (W.sub.2b, W.sub.2a), (W.sub.3b,
W.sub.3a), (W.sub.4b, W.sub.4a), and illustrates a value w'.sub.b
for the first shape measure is found by interpolation from a value
w'.sub.a for the second shape measure. The lookup table may store
pairs of values first and second measures and the EDWN retrieve
pairs for interpolation to find expect value of one shape measure
given measured value of another shape measure.
[0191] It is noted here that the method does not determine the
relative extent on basis of an extent to which a sequence of
gradient across the edge departs from a perfect reflection
symmetry. In particular, as described immediately under the present
heading with reference to FIGS. 23A and 23B, which each plots a
perfectly symmetrical sequence of gradients of an edge, there are
edges the method will discriminate against despite the edges having
perfect reflection symmetry in their respective sequences of
gradients across themselves. It will be clear from the examples
later in this section that having perfect reflection symmetry in a
sequence of gradients across the edge will not prevent an edge from
being discriminated against (i.e. having its relative extent
reduced).
[0192] More specifically, there is a sequence of gradients having
perfect reflection symmetry such that if an edge has the sequence
of gradients across itself then its relative extent will be reduced
under this method. As an example, such a sequence may be {0.1,
0.15, 0.2, 0.9, 0.9, 1, 0.9, 0.9, 0.2, 0.15, 0.1}. As another
example, such sequence may be {0, 0.2, 0.2, 0.7, 0.7, 1, 0.7, 0.7,
0.2, 0.2, 0}, which is sequence 8002 shown in FIG. 27A to take a
shape of a hat. As a third example, such a sequence may be {0,
0.25, 0.5, 0.75, 1, 0.75, 0.5, 0.25, 0}, which is shown as sequence
8000 in FIG. 27B in a shape of a isosceles triangle.
[0193] One of the shape measures may be the edge-sharpness measure,
but this is not necessary. Where the edge-sharpness measure is not
one of the shape measures and the edge is disqualified (i.e.
omitted) from contributing to the focus signal, computing of the
edge-sharpness measure for the edge may be omitted.
[0194] The shape measures are mutually independent in a sense that
any shape measure cannot be computed from the other shape measures
without further involving at least one sample of image data from
the predetermined neighborhood of the edge for which said any shape
measure is computed.
[0195] Preferably, a shape measure is not computed from one
positive gradient and one negative gradient for every edge for
which the shape measure is computed. For most edges, to find an
interpolated gradient on the edges does not require interpolating
between a positive gradient and a negative gradient.
[0196] Preferably, evaluating a shape measure for an edge does not
depend upon detection of another edge. Frequently, an edge has its
own distribution of normalized gradients that is independent of
another edge, and a shape measure formulated based such
characteristic of the edge is not affected by detection or not of
the other edge, especially if the predetermined neighborhood of the
other edge and this edge do not overlap.
[0197] Preferably, a shape measure is not chosen to measure an edge
unless a 20% decrease the scene will not result in difference
between whether the edge is omitted or allowed to contribute to the
focus signal. Alternatively, a shape measure is preferably not
chosen to measure an edge unless a 20% decrease in the image signal
values within the predetermined neighborhood will not result in
whether the edge is omitted or accepted to contribute to the focus
signal.
[0198] In general, any of the methods described under the heading
"Edge-sharpness measures" for computing a edge-sharpness measure
can be used to create one or more shape measures, as long as all
shape measures and the edge-sharpness are computed differently. For
example, with reference to FIG. 23B, a first shape measure may be
the width W.sub.2 between two interpolated gradients at the upper
normalized gradient level 3310 and a second shape measure the width
W.sub.1 measured between a pair of interpolated gradients at the
lower gradient level 3312. The second shape measure may also be
used as the edge-sharpness measure for the edge of this example.
Alternatively, the edge-sharpness measure may be measure at a third
normalized gradient level different than the upper 3310 and lower
3312 normalized gradient levels. Still alternatively, either the
second measure or the edge-sharpness measure may be measured using
another shape measure method, for example as a function of
distances of the gradients over the edge from a predefined position
(e.g. a center of gravity), where a second moment of the distances
is one example of such function of distances of the gradients, or
for example as a distance between the outer pair of interpolated
second derivatives interpolated from a sequence of second
derivatives arrayed across the edge.
[0199] The example discussed above with respect to FIG. 23B and
using two widths (or even pixel counts, which is a quantized
distance quantized to the spacing between adjacent pixels) measure
at two different gradient levels, other combinations are possible,
as discussed further examples below.
[0200] Furthermore, any of the methods to make edge-sharpness
measure that uses a normalizing with respect to a power of a peak
gradient value or interpolated peak gradient value may bypass the
normalizing to generate a shape measure that is not free of a unit
of energy within the unit of the shape measure. For example, where
the edge-sharpness measure is made by measuring an area of a region
under a sequence of gradients (see FIG. 24B and its related
discussion under the heading "Edge-sharpness measures") and
normalizing the area by a peak gradient value or interpolated peak
gradient value, the normalizing may be avoided, resulting in a area
for a shape measure having a unit of a unit of gradient times a
unit of length, thus a unit of energy.
[0201] In addition to any one of the edge-sharpness measure
methods, a shape measure can draw on other methods. Further
examples are described below.
[0202] FIG. 24A illustrates a symmetrical sequence of gradients
across an edge plotted against distance in multiples of a spacing
between successive gradients, and a normalized gradient value of an
interpolated gradient at a predefined distance D.sub.3 from a peak
gradient. This sequence of gradients has a peak gradient 3212. A
width W.sub.0 measured at normalized gradient level L.sub.0 can be
used as the second shape measure. The distance D.sub.3 may be
defined with respect to width W.sub.0, for example as a
predetermined fraction of the width W.sub.0, or with respect to
another width of the edge measured from any one of the
edge-sharpness measures, for example as a predetermine multiple of
a square-root of a variance of the distances of the gradients from
a center of gravity of the gradients. At the distance D.sub.3, a
normalized interpolated gradient value L.sub.3 is computed. This
interpolated gradient value will vary for different sequences of
gradients that have the same width W.sub.0 at the gradient level
L.sub.0 but whose gradient values decline faster with distance from
the peak gradient 3212 than the present sequence of gradient shown
in FIG. 24A. Hence the pair (D.sub.3, W.sub.0) may be check against
a criterion to find the relative extent for an edge from its
sequence of gradient.
[0203] FIG. 24C illustrates a sequence of gradients of an image
signal across an edge plotted against distance in multiples of a
spacing between successive gradients, and a slope S.sub.L (i.e.
normalized second derivative of the image signal) of the plotted
sequence of normalized gradients taken at a normalized gradient
level 3274 defined with respect of an interpolated peak 3270.
Between this sequence of gradients and the sequence of gradients
shown in FIG. 24A, it is clear that for the same width at the
normalized gradient level L.sub.0, the slope measured at this level
or another normalized gradient level will be different between the
shape of the present sequence of gradients and the one shown in
FIG. 24A. These sequences cannot both be good for generating the
focus signal. Therefore the slope is also a candidate method for a
shape measure.
[0204] As another method for a shape measure, a sum of a power of a
plurality of normalized gradient values from a sequence of
gradients across the edge can be used. Additionally, if this sum is
further normalized by the same power of a peak gradient value or of
an interpolated peak's gradient value, it can be used as a
edge-sharpness measure and has a unit of a reciprocal of a unit of
length.
[0205] Between a first shape measure and a second shape measure
used for determining the relative extent of the edge, both shape
measures may be chosen such that both are not affected by scaling
the samples of image data from the aforementioned predetermined
neighborhood of the edge. For example, as in the above discussion
with reference to FIG. 23B, both widths W.sub.1 and W.sub.2 are not
affected by scaling the image data that enter the computation of
the gradients in the sequence of gradients displayed.
Alternatively, both measure be chosen so that they are both
affected by same. For example, the first measure may be the
interpolated gradient value L.sub.3 at a predefined distance
D.sub.3 from an interpolated peak or a peak gradient of the
sequence of gradients, as shown in FIG. 24A and discussed above,
and the second measure may be an area of a region under the
sequence of gradients plotted against distance, as shown in FIG.
24B and discussed above, but without normalizing.
[0206] It is noted that, in this disclosure, a quantity from an
edge, such as a gradient level, is said to be normalized when it is
divided by, by default unless otherwise specified, either a peak
gradient value of the edge or gradient value of an interpolated
peak. For example, in FIG. 23B, peak gradient 3212 has a normalized
value of exactly 1, whereas in FIG. 24C the interpolated peak 3270
is different from the peak gradient 3212, and the gradients shown
in FIG. 24C are normalized with respect to the interpolated peak
3270, not the peak gradient 3212.
Length Filter
[0207] Below describes a function of length filter 212. Broadly
defined, length filter 212 creates a preference for edges that each
connects to one or more edges of a similar orientation. A group of
edges that are similarly oriented and mutually connected within the
group ("concatenated edge") is less likely to be due to noise,
compared with an isolated edge that does not touch any other edge
of similar orientation. The more edges of a similar orientation
thus concatenated together, the lesser the chance of them being due
to noise. The probability of the group being due to noise falls off
exponentially as the number of edges within the group increases,
and far faster than linearly. This property can be harnessed to
reject noise, especially under dim-lit or short-exposure situations
where the signal-to-noise ratio is weak, e.g. less than 10, within
the image or within the region of interest. The preference may be
implemented in any reasonable method to express such preference.
The several ways described below are merely examples.
[0208] A first method is to eliminate edges that belong to
vertical/horizontal concatenated edges having lengths lesser than a
concatenated length threshold. The concatenated length threshold
may be larger when the region of interest is dimmer. For example,
the concatenated length threshold may start as small as 2, but
increases to 8 as a signal-to-noise ratio within the region of
interest drops to 5. The concatenated length threshold may be
provided by the processor 112, 112', 112'', for example through a
`length command` signal, shown in FIG. 3, as part of signals 132.
Alternately, the threshold may be calculated according to a formula
on the focus signal generator.
[0209] A second method is to provide a length-weight in the length
filter 212 for each edge and apply the length-weight to a
calculation of focus signal in the focus signal calculator 210. An
edge that is part of a longer concatenated edge receives a larger
weight than one that is part of a shorter concatenated edge. For
example, the length-weight may be a square of the length of the
concatenated edge. Thus, a contribution of each edge towards the
focus signal may be multiplied by a factor A/B before summing all
contributions to form the focus signal, where B is a sum of the
length-weights of all edges that enter the focus signal
calculation, and A is a length-weight of the edge. Likewise, the
edge-width histogram, which may be output as part of signals 134,
may have edges that are members of longer concatenated edges
contribute more to the bins corresponding to their respective edge
width, thus preferred, instead of all edges contribute the same
amount, e.g. +1. Thus, for example, each edge may contribute A/C,
where C is an average value of A across the edges. Similarly, the
narrow-edge count may have edges that are members to longer
concatenated edges contribute more. Thus, for example, the
contribution from each edge may be multiplied by A/D, where D is an
average of A among edges that are counted in the narrow-edge
count.
[0210] A group of N vertical (horizontal) edges where, with the
exception of the top (leftmost) and the bottom (rightmost) ones,
each edge touches two other vertical (horizontal) edges, one above
(to the left of) itself, the other below (to the right of) itself,
is a vertical (horizontal) concatenated edge of length N. The top
(leftmost) edge needs only touch one edge below (to the right of)
itself. The bottom (rightmost) edge needs only touch one edge above
(to the left of) itself.
[0211] FIG. 8 illustrates a vertical concatenated edge and its
length. In FIG. 8, cells R2C3 and R2C4 form a first vertical edge,
cells R3C3, R3C4, and R3C5 together form a second vertical edge,
and cells R4C4 and R4C5 together form a third vertical edge. The
first and the third vertical edges each touches only one other
vertical edge, whereas the second vertical edge touches two other
vertical edges. The first, second and third vertical edges together
form a vertical concatenated edge having a length of 3.
[0212] In a situation (not shown) where a vertical (horizontal)
concatenated edge has two or more branches, i.e. having two edges
in a row (column), the length may be defined as the total number of
edges within the concatenated edge. Alternately, the length may be
defined as the vertical (horizontal) distance from a topmost
(leftmost) edge therein to a bottommost (rightmost) edge therein
plus one.
[0213] There are other possible ways to define a concatenated
length other than the above proposals. For example, a definition of
a length for a concatenated edge shall have a property that the
length is proportional to the number of member edges within the
concatenated edge at least up to three. This is to be consistent
with the previously stated reasoning that more edges being mutually
connected by touching each other exponentially reduces a
probability that the concatenated edge is caused by a noise, and as
such the length should express a proportionality to the number of
member edges within the concatenated edge up to a reasonable number
that sufficiently enhances a confidence in the concatenated edge
beyond that for a single member. The length filter 212 may
de-emphasize or eliminate and thus, broadly speaking, discriminate
against an edge having a concatenated length of one. The length
filter 212 may discriminate against an edge having a concatenated
length of two. The length filter 212 may discriminate against an
edge having a concatenated length of three, to further reduce an
influence of noise. The length filter 212 may do any one of these
actions under a command from the processor.
[0214] Although shown in FIG. 3 to immediately follow the Edge
Detection & Width Measurement Unit 206, other arrangements are
possible. For example, the Length Filter 212 may be inserted before
the focus signal calculator 210, wherein the edges processed by the
Length Filter 212 are those that pass through the width filter 209
depending on the `fine` signal.
[0215] In an alternate embodiment of a focus signal generator, the
fine switch 220 may be removed so that the focus signal calculation
unit 210 receives a first set of data not filtered by the width
filter 209 and a second set filtered, and for each calculates a
different focus signal, gross focus signal for the former, fine
focus signal for the latter, and outputs both to the processor 112,
112'.
Width Filter
[0216] Refer next to FIG. 3 to understand an operation of the Width
Filter 209. FIG. 11 plots a histogram of edge widths, i.e. a graph
of edge counts against edge widths. At edge width of 2, i.e. the
aforementioned sharp_edge_width, there is a peak, indicating a
presence of sharp edges in the image. At edge widths of 4 and 5,
however, there are peaks, indicating edges that are blurred,
possibly due to the corresponding imaged objects being out of
focus, being at a different distance away from the focus lens than
those objects that give rise to the sharp edges. For calculating a
focus signal, edges whose widths lie outside a predetermined range
("narrow-edge range") may be de-emphasized using the Width Filter
209. The Width Filter 209 may create a lesser weight for edge
widths outside the narrow-edge range for use in the focus signal
calculation. For example, edge widths may be assigned weight of
1.0, whereas edges widths more than +1 to the right of the upper
limit 840 assigned a weight of 0, and edge widths in between
assigned weights between 0 and 1.0, falling monotonically with edge
width. Alternately, the Width Filter 209 may prevent such edges
from entering the focus signal calculation altogether. Appropriate
upper and lower limits 830, 840 depend on several factors,
including crosstalk in the pixel array 108, the interpolation
method used to generate missing colors for the image received by
the focus signal generator 120, and the filter coefficients used in
the lowpass filter employed in the Edge Detection and Width
Measurement Unit 206. Appropriate upper and lower limits 830, 840
and the parameter sharp_edge_width may be determined for the image
pickup apparatus 102, 102' by capturing images of various degrees
of sharpness and inspecting the edge width histograms. For example,
if a sharp image has a peak at edge width of 2, an appropriate
lower and upper limit may be 1.5 and 3, respectively, and the
sharp_edge_width may be set to 2.0. The lower and upper limits and
sharp_edge_width may be determined as above and provided to the
focus signal generator 120, 120', 120'' by the processor 112,
112''. When `fine command` is ON, the fine focus signal thus
calculated de-emphasizes edge widths outside the narrow-edge
range.
[0217] In addition, the Width Filter 209 may calculate a total
count of the edges whose edge widths fall within the narrow-edge
range and output as part of output signals 134.
[0218] Narrow-Edge Count may be input to and used by the focus
system controller (processor 112) to detect a presence of sharp
image and/or for initiating tracking.
Focus Signal
[0219] Referring next to the focus signal calculator 210 of
[0220] FIG. 3, the focus signal calculator 210 receives edge widths
and outputs a focus signal. The focus signal may be calculated as a
weighted average of all the edge widths where the weights are the
edge counts for each edge width, viz. focus
signal=.SIGMA.w.sub.ie.sub.i/.SIGMA.w.sub.i, where e.sub.i are the
edge widths, w.sub.i are the weights, where here w.sub.i=c.sub.i,
c.sub.i being the edge count at edge width e.sub.i, i being a bin
number of a histogram of edge widths. Alternately, the weight at
each edge width may be the edge count for the edge width multiplied
by the edge width itself, i.e. w.sub.i=c.sub.ie.sub.i. In addition,
preferences from the Width Filter 209 that are expressed in terms
of weights may be further multiplied to each edge width. For
example, for weights .OMEGA..sub.i produced by the Width Filter
209, .SIGMA..OMEGA..sub.i=1, focus signal may be calculated as
.SIGMA..OMEGA..sub.iw.sub.ie.sub.i/.SIGMA..OMEGA..sub.iw.sub.i. If
control signal `fine` is ON and `exclude` is OFF, the focus signal
would be a value very close to the sharp_edge_width of 2.0 for the
example shown in FIG. 11, indicating that among object details
within the focus distance range that would produce edge widths
between 2.0 and 3.0, most are actually in sharp focus. If control
signal `fine` is OFF and `exclude` is OFF, the focus signal may be
a value close to 5.0, indicating that there are substantial details
of the image that are out of focus. Turning ON the fine switch 220
allows the focus signal to respond more to objects slightly blurred
while less to those that are completely blurred. When the fine
switch 220 is ON, we shall refer to the focus signal as a fine
focus signal, whereas when the fine switch 220 is OFF, a gross
focus signal. As aforementioned, the emphasis expressed by the
Length Filter 212 may be incorporated into the focus signal in one
of several ways, such as eliminating an edge that is de-emphasized
from entering the focus signal calculation, or reducing a weight of
the edge's contribution towards a count e.sub.i of a corresponding
edge width bin.
[0221] FIG. 15 sketches a response of the fine focus signal to an
adjustment of the focus position in the vicinity of where an object
is in sharp focus. The fine focus signal reaches a minimum value,
approximately at sharp_edge_width, where the focus position brings
an image into sharp focus, and increases if otherwise. The fine
focus signal may be used for tracking objects already in-focus or
very nearly so. For moving objects, the fine focus signal allows
the focus control system to keep the objects in sharp focus even if
the focus distance continues to change. Fine focus signal may also
be used to acquire a sharp focus ("acquisition") of an object that
is not yet in sharp focus but close enough such that the object
gives rise to edges whose widths fall within the narrow-edge range.
Since the edge width histogram exhibits a peak at the edge width
corresponding to the object away from the sharp_edge_width,
resulting in the fine focus signal being larger than the
sharp_edge_width, the focus control system may respond by adjusting
the focus position to bring the fine focus signal value towards the
sharp_edge_width, thus centering the peak of edge width due to the
object at the edge width value equal to sharp_edge_width.
Basic Use
[0222] FIGS. 12-16 illustrate how the narrow-edge count, gross
focus signal, and fine focus signal may be used to perform focus
control to achieve sharp images.
[0223] FIG. 12 illustrates an outdoor scene having 3 groups of
objects at different focus distances: "person" in the foreground,
"mountain, sun, and horizon" in the background, and "car" in the
between.
[0224] FIG. 13 is an illustration of the narrow-edge count plotted
against time when the focus position of the focus lens 104 sweeps
from far to near for the scene illustrated in FIG. 12. The
narrow-edge count peaks when the focus position brings an object
into a sharp image on the pixel array 108. Thus the narrow-edge
count plot exhibits 3 peaks, one each for "mountain, sun, and
horizon", "car", and "person", in this order, during the sweep.
[0225] FIG. 14 shows the gross focus signal plotted against time.
The gross focus signal exhibits a minimum when the focus position
is near each of the 3 focus positions where the narrow-edge count
peaks. However, at each minimum, the gross focus signal is not at
the sharp_edge_width level, which is 2.0 in this example, due to
bigger edge widths contributed by the other objects that are
out-of-focus.
[0226] FIG. 15 illustrates the fine focus signal plotted against
the focus position in the vicinity of the sharp focus position for
"car" in the scene of FIG. 12. The fine focus signal achieves
essentially the sharp edge width, which is 2 in this example,
despite the presence of blurred objects ("person" and "mountains,
sun, and horizon"). Referring to FIG. 11 again, where two peaks at
widths of 4 and 5 are contributed by those two groups of blurred
objects, this can be understood as the Width Filter 324 having
reduced the weight or eliminated altogether the contributions from
the edge widths to the right of upper-limit 840.
[0227] A focus control system may use the gross focus signal to
search for the nearest sharp focus position in a search mode. It
can move the focus position away from the current focus position to
determine whether the gross focus signal increases or decreases.
For example, if the gross focus signal increases (decreases) when
the focus position moves inwards (outwards), there is a sharp focus
position farther from the current focus position. The processor
112, 112', 112'' can then provide a focus drive signal to move the
focus lens 104 in the direction towards the adjacent sharp focus
position.
[0228] A focus control system may use the fine focus signal to
track an object already in sharp focus to maintain the
corresponding image sharp (thus a "tracking mode") despite changes
in the scene, movement of the object, or movement of the image
pickup apparatus. When an object is in sharp focus, the fine focus
signal level is stable despite such changes. Hence a change in the
fine focus signal suggests a change in focus distance of the object
from the image pickup apparatus. By "locking" the focus control
system to a given fine focus signal level near the minimum, for
example between 2.0 to 2.5 in this example, in particular 2.1, any
shift in the fine focus signal level immediately informs the
processor 112, 112', 112'' of a change in the focus distance of the
object. The processor 112, 112', 112'' can then determine a
direction and cause the focus lens 104 to move to bring the fine
focus signal level back to the "locked" level. Thus the image
pickup apparatus 102, 103, 103', 103'' is able to track a moving
object.
[0229] A focus control system, e.g. as implemented in algorithm in
processor 112, 112', 112'', may use narrow-edge count to trigger a
change from a search mode to a tracking mode. In the tracking mode,
the focus control system uses the fine focus signal to "lock" the
object. Before the focus position is sufficiently near the sharp
focus position for the object, the focus control system may use the
gross focus signal to identify the direction to move and regulate
the speed of movement of the lens. When a object is coming into
sharp focus, narrow-edge count peaks sharply. The processor 112,
112', 112'' may switch into the tracking mode and use the fine
focus signal for focus position control upon detection of a sharp
rise in the narrow-edge count or a peaking or both. A threshold,
which may be different for each different sharp focus position, may
be assigned to each group of objects found from an end-to-end focus
position "scan", and subsequently when the narrow-edge count
surpasses this threshold the corresponding group of objects is
detected. For a stationary scene, e.g. for still image taking, an
end-to-end focus position scan can return a list of maximum counts,
one maximum count for each peaking of the narrow-edge count. A list
of thresholds may be generated from the list of maximum counts, for
example by taking 50% of the maximum counts.
[0230] FIG. 16 illustrates an image pickup apparatus 102 having a
display 114, an input device 107 comprising buttons, and selection
marker 1920 highlighted in the display 114. A user can create,
shape and maneuver the selection marker 1920 using input device
107. Although shown in this example to comprise buttons, input
device 107 may comprise a touch-screen overlaying the display 114
to detect positions of touches or strokes on the display 114. Input
device 107 and processor 112, 112', 112'' or a separate dedicated
controller (not shown) for the input device 107 may determine the
selection region. The parameters for describing the selection
region may be transmitted to the focus signal generator 120, 120',
120'' over bus 132 (or internally within the processor 112 in the
case where focus signal generator 120 is part of the processor
112). In response, the focus signal generator 120 may limit the
focus signal calculation or the narrow-edge count or both to edges
within the selection region described by said parameters or
de-emphasize edges outside the selection region. Doing so can
de-emphasize unintended objects from the focus signal and then even
the gross focus signal will exhibit a single minimum and a minimum
level within 1.0 or less of the sharp_edge_width.
Alternate Embodiments
[0231] FIG. 17 shows an alternate embodiment of a focus signal
generator 120'. Focus signal generator 120' outputs statistics of
edges and edge widths. Among the edge-width statistics that
controller 120' outputs may be one or more of the following: an
edge-width histogram comprising edge counts at different edge
widths; an edge width where edge width count reaches maximum; a set
of coefficients representing a spline function that approximates
edge counts at different edge widths; and any data that can
represent a function of edge width. Census Unit 240 may receive
data computed in one or more of the other units with the focus
signal generator 120' to calculate statistics of edge widths. In
general, the focus signal generator 120' may output a signal that
has an indication of a distribution of edge widths.
[0232] Referring to FIG. 18, the edge-width statistics thus
provided in signals 134 to an alternative embodiment of processor
112' in an alternative auto-focus image pickup apparatus 102' may
be used by the processor 112' to compute a gross and/or fine focus
signal and a narrow-edge count in accordance with methods discussed
above or equivalent thereof. In addition, any data computed in the
focus signal generator 120' may be output to the processor 112' as
part of the output signals 134.
[0233] The processor 112' may internally generate a focus signal
and/or a narrow-edge count in addition to the functions included in
the processor 112 of FIG. 1.
[0234] The pixel array 108, A/D Converter 110, color interpolator
148, and generator 120' may reside within a package 142, together
comprising an image sensor 150', separate from the processor
112'.
Auxiliary Pixel Array
[0235] FIG. 19 shows an alternate embodiment of an auto-focus image
pickup system 103. In addition to elements included in a system
102, the system 103 may include a partial mirror 2850, a full
mirror 2852, an optical lowpass filter 2840, a main pixel array
2808, and a main A/D Converter 2810. The partial mirror 2850 may
split the incoming light beam into a first split beam and a second
split beam, one transmitted, the other reflected. The first split
beam may further pass through the optical lowpass filter 2840
before finally reaching the main pixel array 2808, which detects
the first split beam and converts to analog signals. The second
split beam may be reflected by the full mirror 2852 before finally
reaching the auxiliary pixel array 108'', which corresponds to the
pixel array 108 in system 102 shown in FIG. 1. The ratio of light
intensity of the first beam to the second beam may be 1-to-1 or
greater than 1-to-1. For example, the ratio may be 4-to-1.
[0236] The main pixel array 2808 may be covered by a color filter
array of a color mosaic pattern, e.g. the Bayer pattern. The
optical lowpass filter 2808 prevents the smallest light spot
focused on the pixel array 2808 from being too small as to cause
aliasing. Where a color filter of a mosaic pattern covers the pixel
array 2808, aliasing can give rise to color moire artifacts after a
color interpolation. For example, the smallest diameter of a circle
encircling 84% of the visible light power of a light spot on the
main pixel array 2808 ("smallest main diameter") may be kept larger
than one and a half pixel width but less than two pixel widths by
use of the optical lowpass filter. For example, if the main pixel
array 2808 has a pixel width of 4.5 um, whereas the smallest
diameter is 2.0 um without optical lowpass filtering, the optical
lowpass filter 2840 may be selected to make the light spot 6.7 um
or larger in diameter.
[0237] The auxiliary pixel array 108'' may comprise one or more
arrays of photodetectors. Each of the arrays may or may not be
covered by a color filter array of a color mosaic pattern. The
array(s) in auxiliary pixel array 108'' outputs image(s) in analog
signals that are converted to digital signals 130 by A/D Converter
110. The images are sent to the focus signal generator 120. A color
interpolator 148 may generate the missing colors for images
generated from pixels covered by color filters. If auxiliary pixel
array 108'' comprises multiple arrays of photodetectors, each array
may capture a sub-image that corresponds to a portion of the image
captured by the main pixel array 2808. The multiple arrays may be
physically apart by more than a hundred pixel widths, and may or
may not share a semiconductor substrate. Where the pixel arrays
within auxiliary pixel array 108'' do not share a semiconductor
substrate, they may be housed together in a package (not
shown).
[0238] Main A/D Converter 2810 converts analog signals from the
Main Pixel Array 2808 into digital main image data signal 2830,
which is sent to the processor 112, where the image captured on the
Main Pixel Array 2808 may receive image processing such as color
interpolation, color correction, and image
compression/decompression and finally be stored in memory card
116.
[0239] An array of photodetectors in the auxiliary pixel array
108'' may have a pixel width ("auxiliary pixel width") that is
smaller than a pixel width of the main pixel array 2808 ("main
pixel width"). The auxiliary pixel width may be as small as half of
the main pixel width. If an auxiliary pixel is covered by a color
filter and the auxiliary pixel width is less than 1.3 times the
smallest spot of visible light without optical lowpass filtering, a
second optical lowpass filter may be inserted in front of the
auxiliary array 108'' to increase the smallest diameter on the
auxiliary pixel array 108'' ("smallest auxiliary diameter") to
between 1.3 to 2 times as large but still smaller than the smallest
main diameter, preferably 1.5. The slight moire in the auxiliary
image is not an issue as the auxiliary image is not presented to
the user as the final captured image.
[0240] FIG. 22 illustrates how edge widths may vary about a sharp
focus position for main images from the main pixel array 2808
(solid curve) and auxiliary images from the auxiliary pixel array
108'' (dashed curve). The auxiliary images give sharper slopes even
as the main images reach the targeted sharp_edge_width of 2. The
auxiliary image is permitted to reach below the targeted
sharp_edge_width, since moire due to aliasing is not as critical in
the auxiliary image, as it is not presented to the user as a final
image. This helps to sharpen the slope below and above the
sharp_edge_width. The sharper slope is also helped by the auxiliary
pixel width being smaller than the main pixel width.
[0241] The shaded region in FIG. 22 indicates a good region within
which to control the focus position to keep the main image in sharp
focus. A change in focus position outwards will cause the edge
width to increase in the auxiliary image, whereas a change inwards
will cause the it to decrease. To maintain the main image's edge
widths near the sharp_edge_width, a linear feedback control system
may be employed to target the middle auxiliary edge width value
within the shade region and to use as feedback signal the edge
widths generated from the auxiliary images.
[0242] The auxiliary pixel array 108'', A/D Converter 110, focus
signal generator 120 together may be housed in a package 142 and
constitute an auxiliary sensor 150. The auxiliary sensor 150 may
further comprise a color interpolator 148.
[0243] FIG. 20 shows an alternative embodiment of auto-focus image
pickup apparatus 103' similar to apparatus 103 except focus signal
generator 120' replaces focus signal generator 120. The auxiliary
pixel array 108'', A/D Converter 110, focus signal generator 120'
together may be housed in a package 142 and constitute an auxiliary
sensor 150'. The auxiliary sensor 150 may further comprise a color
interpolator 148.
[0244] FIG. 21 shows an alternate embodiment of auto-focus image
pickup apparatus 103''. The focus signal generator 120 and the
processor 112'' may be housed in a package 144 as a camera
controller, separate from the auxiliary pixel array 108''. The
processor 112'' is similar to processor 112 except that processor
112'' receives images from the main pixel array 2808 as well as the
auxiliary pixel array 108''. The processor 112'' may perform a
color interpolation, a color correction, a
compression/decompression, and a storing to memory card 116 for the
images received on signal 2830 similar to the processing that the
processor 112 may perform on signal 130 in FIG. 2. Unlike in FIG.
2, here the images received on signal 130 need not receive
compression/decompression and storing to memory card 116. The
processor 112'' may perform color interpolation on images received
on signal 130 for pixels that are covered by color filters in the
auxiliary pixel array 108'' and send the color interpolated images
to the focus signal generator 120 on signal 146.
[0245] The auto-focus image pickup system 102, 102', 103, 103',
103'' may include a computer program storage medium (not shown)
that comprises instructions that causes the processor 112, 112',
112'' respectively, and/or the focus signal generator 120, 120' to
perform one or more of the functions described herein. By way of
example, the instructions may cause the processor 112 or the
generator 120' to perform a slant correction for an edge width in
accordance with the flowchart of FIG. 7. As another example, the
instructions may cause the processor 112' or the generator 120 to
perform an edge width filtering in accordance with the above
description for Width Filter 209.
[0246] Alternately, the processor 112, 112' or the generator 120,
120' may be configured to have a combination of firmware and
hardware, or a pure hardware implementation for one or more of the
functions contained therein. For example, in generator 120, a slant
correction may be performed in pure hardware and a length filter
212 performed according to instructions in a firmware.
[0247] FIG. 30 shows yet another embodiment of focus signal
generator 120'. This embodiment may be employed in any of the above
image capture systems.
[0248] While a memory card 116 is shown as part of system 102, any
nonvolatile storage medium may be used instead, e.g. hard disk
drive, wherein images stored therein are accessible by a user and
may be copied to a different location outside and away from the
system 102.
[0249] One or more parameters for use in the system, for instance
the sharp_edge_width, may be stored in a non-volatile memory in a
device within the system. The device may be a flash memory device,
the processor, or the image sensor, or the focus signal generator
as a separate device from those. One or more formulae for use in
the system, for example for calculating the concatenated length
threshold, or for calculating beta may likewise be stored as
parameters or as computer-executable instructions in a non-volatile
memory in one or more of those devices.
[0250] While certain exemplary embodiments have been described and
shown in the accompanying drawings, it is to be understood that
such embodiments are merely illustrative of and not restrictive on
the broad invention, and that this invention not be limited to the
specific constructions and arrangements shown and described, since
various other modifications may occur to those ordinarily skilled
in the art.
* * * * *