Methods and apparatuses providing noise reduction while preserving edges for imagers

Abstract

Methods and apparatuses of reducing noise in an image by obtaining a first value for a target pixel, obtaining a respective second value for neighboring pixels surrounding the target pixel, for each neighboring pixel, comparing a difference between the first value and the second value to a threshold value and selectively replacing the first value with an average value obtained from the first value and at least a subset of the second values from the neighboring pixels which have an associated difference which is less than the threshold value based on a result of the comparing step. In a further modification, less than all neighboring pixels which have an associated difference which is less than the threshold value are used in the averaging.

Description

[0001] This application is a continuation-in-part of U.S. patent application Ser. No. 11/295,445, filed on Dec. 7, 2005, the subject matter of which is incorporated in its entirety by reference herein.

FIELD OF THE INVENTION

[0002] The embodiments described herein relate generally to the field of solid state imager devices, and more particularly to methods and apparatuses for noise reduction in a solid state imager device.

BACKGROUND OF THE INVENTION

[0003] Solid state imagers, including charge coupled devices (CCD), CMOS imagers and others, have been used in photo imaging applications. A solid state imager circuit includes a focal plane array of pixels, each one of the pixels including a photosensor, which may be a photogate, photoconductor or a photodiode having a doped region for accumulating photo-generated charge.

[0004] One of the most challenging problems for solid state imagers is noise reduction, especially for imagers with a small pixel size. The effect of noise on image quality increases as pixel sizes continue to decrease and may have a severe impact on image quality. Specifically, noise impacts image quality in smaller pixels because of reduced dynamic range. One of the ways of solving this problem is by improving fabrication processes; the costs associated with such improvements, however, are high. Accordingly, engineers often focus on other methods of noise reduction. One such solution applies noise filters during image processing. There are many complicated noise reduction algorithms which reduce noise in the picture without edge blurring, however, they require huge calculating resources and cannot be easily implemented in a system-on-a-chip application. Most simple noise reduction algorithms which can be implemented in system-on-a-chip applications blur the edges of the images.

[0005] Two known methods that may be used for image denoising are briefly now discussed. The first method includes the use of local smoothing filters, which work by applying a local low-pass filter to reduce the noise component in the image. Typical examples of such filters include averaging, medium and Gaussian filters. One problem associated with local smoothing filters is that they do not distinguish between high frequency components that are part of the image and those created due to noise. As a result, these filters not only remove noise but also blur the edges of the image.

[0006] A second group of denoising methods work in the spatial frequency domain. These methods typically first convert the image data into a frequency space (forward transform), then filter the transformed image and finally convert the image back into the image space (reverse transform). Typical examples of such filters include DFT filters and wavelength transform filters. The utilization of these filters for image denoising, however, is impeded by the large volume of calculations required to process the image data. Additionally, block artifacts and oscillations may result from the use of these filters to reduce noise. Further, these filters are best implemented in a YUV color space (Y is the luminance component and U and V are the chrominance components). Accordingly, there is a need and desire for an efficient image denoising method and apparatus which does not significantly blur the edges of the image.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 is a top-down view of a conventional microlens and color filter array used in connection with a pixel array.

[0008] FIG. 2A depicts an image correction kernel for a red, greenred, greenblue, or blue pixel of a pixel array in accordance with an embodiment.

[0009] FIG. 2B depicts a correction kernel for a green pixel of a pixel array in accordance with an embodiment.

[0010] FIG. 3 depicts the correction kernel of FIG. 2A in more detail.

[0011] FIG. 4 shows a flowchart of a method for removing pixel noise in accordance with an embodiment.

[0012] FIG. 5 shows a flowchart of a method for removing pixel noise in accordance with another embodiment.

[0013] FIG. 6 shows a block diagram of an imager constructed in accordance with an embodiment described herein.

[0014] FIG. 7 shows a processor system incorporating at least one imager constructed in accordance with an embodiment described herein.

DETAILED DESCRIPTION OF THE INVENTION

[0015] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof and show by way of illustration specific embodiments that may be practiced. These embodiments are described in sufficient detail to enable those of ordinary skill in the art to make and use them, and it is to be understood that other embodiments may be utilized, and that structural, logical, procedural, and electrical changes may be made to the specific embodiments disclosed. The progression of processing steps described is an example of the embodiments; however, the sequence of steps is not limited to that set forth herein and may be changed as is known in the art, with the exception of steps necessarily occurring in a certain order.

[0016] The term "pixel," as used herein, refers to a photo-element unit cell containing a photosensor device and associated structures for converting photons to an electrical signal. For purposes of illustration, a small representative three-color pixel array is illustrated in the figures and description herein. However, the embodiments may be applied to monochromatic imagers as well as to imagers for sensing fewer than three or more than three color components in an array. Accordingly, the following detailed description is not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims.

[0017] FIG. 1 depicts one known conventional color filter array, arranged in a Bayer pattern, covering a pixel array to focus incoming light. It should be understood that, taken alone, a pixel generally does not distinguish one incoming color of light from another and its output signal represents only the intensity of light received, not any identification of color. However, pixels 80, as discussed herein, are referred to by color (i.e., "red pixel," "blue pixel," etc.) when a color filter 81 is used in connection with the pixel array to focus a particular wavelength range of light, corresponding to a particular color, onto the pixels 80. Accordingly, when the term "red pixel" is used herein, it is referring to a pixel associated with and receiving light through a red color filter; when the term "blue pixel" is used herein, it is referring to a pixel associated with and receiving light through a blue color filter; and when the term "green pixel" is used herein, it is referring to a pixel associated with and receiving light through a green color filter. It should be appreciated that the term "green pixel" can refer to a "greenred pixel," which is a green pixel in the same row with red pixels, and can refer to a "greenblue pixel," which is a green pixel in the same row with blue pixels.

[0018] FIGS. 2A and 2B illustrate parts of pixel array 100 having an identified target pixel 32a, 32b that may undergo a corrective method in accordance with an embodiment described herein. The identified target pixel 32a shown in FIG. 2A in pixel array 100 may be a red, a greenred, a greenblue, or a blue pixel. Pixel array 100 shown in FIG. 2B has an identified pixel 32b that for purposes of further description is a green pixel (either greenred or greenblue).

[0019] In the illustrated examples, it is assumed that the pixel array 100 is associated with a Bayer pattern color filter array 82 (FIG. 1); however, the embodiments may also be used with other color filter patterns or the color filter array may be omitted for a monochrome pixel array 100. The color filters 81 focus incoming light of a particular wavelength range onto the underlying pixels 80. In the Bayer pattern, as illustrated in FIG. 1, every other pixel array row consists of alternating red (R) and green (G) colored pixels, while the other rows consist of alternating green (G) and blue (B) color pixels.

[0020] To denoise identified target pixel 32a, 32b, embodiments utilize signal values of the nearest neighboring pixels of the identified target pixel 32a, 32b. The identified target pixel 32a, 32b is the pixel currently being processed. The neighboring pixels are collectively referred to herein as a correction kernel, shown in FIGS. 2A and 2B respectively as kernels 101a, 101b. For example, it may be desirable to select the pixels in the correction kernel 101a to have the same color as the target pixel 32a, such as, for example, red, greenred, greenblue, and blue and to select the pixels in the correction kernel 101b to have the same color as the target pixel 32b, such as, for example green (without differentiating between greenred and greenblue). A total of eight neighboring pixels are included in each kernel 101a, 101b. It should be noted, that the illustrated correction kernels 101a, 101b are examples, and that other correction kernels may be chosen for pixel arrays using color filter patterns other than the Bayer pattern. In addition, a correction kernel could encompass more or less than eight neighboring pixels, if desired.

[0021] In FIGS. 2A and 2B, the illustrated correction kernels 101a, 101b are outlined with a dotted line. For kernel 101a there are eight pixels (pixels 10, 12, 14, 34, 54, 52, 50, and 30) having the same color as the identified target pixel 32a. Although it appears that correction kernel 101a contains sixteen pixels, it should be noted that half of the pixels are not the same color as the target pixel 32a, whose signals would not be considered for use in denoising target pixel 32a. The actual pixels that make up kernel 101a are shown in greater detail in FIG. 3. Kernel 101b also includes eight pixels (pixels 12, 23, 34, 43, 52, 41, 30, and 21) having the same green color (without differentiating between greenred and greenblue) as the identified pixel 32b.

[0022] As described in detail below, the embodiments described herein may be used to denoise images while preserving edges. Rather than outputting the actual pixel signal value for the target pixel, the target pixel's signal value ("value") is averaged with the signal values of pixels in the correction kernel. This averaging is done to minimize the effect noise has on an individual pixel. For example, in a flat-field image, an array of ideal pixels would output the same signal value for every pixel in the array; however, because of noise the pixels of the array do not output the same signal for every pixel in the array. By averaging the signal values from the surrounding pixels having the same color as the target pixel, the effect of noise on the target pixel is reduced.

[0023] In order to preserve edges, it is desirable to set a threshold such that averaging is only performed if the difference between the target pixel signal value and the signal values of pixels in the correction kernel is below a threshold. Only noise that has amplitude of dispersion (the difference between the average maximum and minimum value) lower than a noise amplitude threshold (TH) will be averaged and reduced. Therefore, the threshold should be set such that noise is reduced, but pixels along edges will be subjected to less (or no) averaging thereby preserving edges. An embodiment described herein sets a noise amplitude threshold (TH), which may be a function of analog and digital gains that may have been applied to amplify the original signal. It should be appreciated that the threshold TH can be varied based on, for example, pixel color. An embodiment described herein accomplishes this by processing a central target pixel by averaging it with all its like color neighbors that produce a signal difference less than the set threshold. Another embodiment described herein accomplishes this by processing a central target pixel by averaging it with a selected subset of its like color neighbors that produce a signal difference less than the set threshold. Further, the exemplary noise filter could be applied either to each color separately in Bayer, Red/Green/Blue (RGB), Cyan/Magenta/Yellow/Key (CMYK), luminance/chrominance (YUV), or other color space.

[0024] With reference to FIG. 4, one example method 200 is now described. The method can be carried out by a processor circuit, such as, for example, an image processor circuit 280 (described below with reference to FIG. 6) which can be implemented in hardware logic, or as a programmed processor or some combination of the two. Alternatively, the method can be implemented by a processor circuit separate from an image processor circuit 280, such as, for example, a separate hardwired logic or programmed processor circuit or a separate stand alone computer.

[0025] It should be understood that each pixel has a value that represents an amount of light received at the pixel. Although representative of a readout signal from the pixel, the value is a digitized representation of the readout analog signal. These values are represented in the following description as P(pixel) where "P" is the value and "(pixel)" is the pixel number shown in FIGS. 2A or 2B. For explanation purposes only, the method 200 is described with reference to the kernel 101a and target pixel 32a as illustrated in FIG. 2A.

[0026] Initially, at step 201, the target pixel 32a being processed is identified. Next, at step 202, the kernel 101a associated with the target pixel 32a is selected/identified. After the associated kernel 101a is selected, at step 203, the difference in values P(pixel) of the central (processed) pixel 32a and each neighboring pixel 10, 12, 14, 30, 34, 50, 52, 54 in kernel 101a are compared with a threshold value TH. The threshold value TH may be preselected, for example, using noise levels from current gain settings, or using other appropriate methods. In the illustrated example, at step 203, neighboring pixels that have a difference in value P(pixel) less than or equal to the threshold value TH are selected. Alternatively, at step 203, a subset of the neighboring pixels that have a difference in value P(pixel) less than or equal to the threshold value TH are selected. For example purposes only, the value could be the red value if target pixel 32a is a red pixel.

[0027] Next, at step 204, a value P(pixel) for each of the kernel pixels located around the target pixel 32a, which were selected in step 203, are added to a corresponding value for the target pixel 32a and an average value A(pixel) is calculated. For example, for target pixel 32a, the average value A32=(P10+P12+P14+P30+P32a+P34+P50+P52+P54)/9 is calculated, if all eight neighboring pixels were selected in step 203. At step 205, the calculated value A(pixel), which is, in this example, A32, replaces the original target pixel value P32a.

[0028] The methods described herein may be carried out on each pixel signal as it is processed. As pixels values are denoised, the values of previously denoised pixels may be used to denoise other pixel values. Thereby, when the method described herein and the values of previously denoised pixels are used to denoise other pixels, the method and apparatus is implemented in a partially recursive manner (pixels are denoised using values from previously denoised pixels). However, the embodiments are not limited to this implementation and may be implemented in a fully recursive (pixels are denoised using values from other denoised pixels) or non-recursive manner (no pixels having been denoised are used to denoise subsequent pixels).

[0029] The method 200 described above may also be implemented and carried out, as discussed above, on target pixel 32b and associated image correction kernel 101b (FIG. 2B). For example, in step 202 the kernel 101b is selected/identified. After the associated kernel 101b is selected for target pixel 32b, the differences in values between each of the neighboring pixels 12, 21, 23, 30, 34, 41, 43, 52 in kernel 101b located around target pixel 32b and the value of target pixel 32b are compared to a threshold TH in step 203. The remaining steps 204, 205 are carried out as discussed above for the pixels corresponding to kernel 101b.

[0030] The methods described above provide good denoising. It may be desirable, however, to limit the number of pixels utilized in the averaging of the target pixel signal value and the correction kernel signal values to decrease implementation time and/or decrease die size. For example, as illustrated in the flowchart of FIG. 5, the number of pixels averaged may, for example, be limited to an integer power of the number two (e.g., 1, 2, 4, 8, etc.) which limits the averaging to binary division. In other words, the average value is an average of 2.sup.n pixel signals where n is an integer. Binary division may be desirable as it can be implemented with register shifts, thereby decreasing die size and time necessary to average the target pixel. The flowchart of FIG. 5 illustrates a method 2000 of noise reduction which can be carried out by an image processor circuit 280 (described below with reference to FIG. 6) which can be implemented in hardware logic or as a programmed processor or some combination of the two. Alternatively, the method can be implemented by a processor circuit separate from an image processor circuit 280, such as, for example, a separate hardwired logic or programmed processor circuit or a separate stand alone computer. For explanation purposes only, the method 2000 is described with reference to the kernel 101a and target pixel 32a as illustrated in FIG. 2A.

[0031] Initially, at step 2010, a target pixel p having a signal value p.sub.sig is selected/identified, for example, pixel 32a (FIG. 2A). It should be appreciated if a Bayer pattern color filter array is utilized with pixel array 100 (FIG. 2A), that pixel 32a may be a red, greenred, greenblue, or blue pixel. For explanation purposes, pixel 32a will be described as and referred to as a greenblue pixel. Next, first and second register values Pixel.sub.sum and Pixel.sub.sum.sub.--.sub.new, respectively, are initialized to be equal to p.sub.sig and first and second counters Pixel.sub.count and Pixel.sub.count.sub.--.sub.new, respectively, are initialed to be equal to 1 (step 2020). Then, a correction kernel associated with the target pixel p containing N pixels is selected/identified, for example, kernel 101a (FIG. 2A) containing greenblue pixels 10, 12, 14, 30, 34, 50, 52, 54 (step 2030). The N pixels from the kernel are grouped at step 2040. It may be desirable to process the correction kernel pixels that are closest to the target pixel first, for example, the N pixels can be grouped into one or more groups g by their distance from target pixel p. For example, a first group g can be selected to include pixels 12, 52, 30, and 34 that are closest to target pixel 32a and a second group g can be selected to include pixels 10, 14, 50, and 54 that are further away from target pixel 32a than the pixels in the first group g. Then the groups g can be assessed in order of their distance to target pixel p, such that the pixels in a group closest to target pixel p can be assessed before pixels in a group further from target pixel p are assessed. It should be appreciated that all of the pixels N can alternatively be grouped into one group g. Then in step 2050, a group g that has not been previously assessed is selected. For example, it may be desirable to select a group of pixels that has not been previously assessed that is closest to target pixel p. Next, a pixel n having a signal value n.sub.sig from the selected group g is selected (step 2060).

[0032] In step 2070, a determination is made to see if the absolute value of the difference between n.sub.sig and P.sub.sig is less than a threshold TH. The threshold value TH may be preselected, for example, using noise levels from current gain settings, or using other appropriate methods. Additionally, the threshold value TH can be preselected based on the color of the target pixel p. If the determined difference is greater than the threshold TH (step 2070), n.sub.sig is not included in the averaging and the method 2000 then determines if all of the pixels in group g have been assessed (step 2130). However, if the determined difference is less than the threshold TH (step 2070), a new value for Pixel.sub.sum is determined by adding n.sub.sig to Pixel.sub.sum (step 2080) and a new value for Pixel.sub.count is determined by incrementing Pixel.sub.count (step 2090). The method 2000 then compares the value of Pixel.sub.count to a set of at least one predetermined number (step 2100). For example, it may be desirable to compare the value of Pixel.sub.count to a set of values comprised of integer powers of the number two. As described below in more detail, division by Pixel.sub.count is required in step 2150 and when implementing division in hardware, division by a power of two can be accomplished with register shifts, thereby making the operation faster and able to be implemented in a smaller die area. If Pixel.sub.count is contained in the set of at least one predetermined number, for example, if Pixel.sub.count is 4 and the set of at least one predetermined number includes 1, 2, 4, and 8, Pixel.sub.count.sub.--.sub.new is determined by setting Pixel.sub.count.sub.--new=Pixel.sub.count (step 2110) and Pixel.sub.sum.sub.--.sub.n, is determined by setting Pixel.sub.sum.sub.--.sub.new=Pixel.sub.sum (step 2120). If Pixel.sub.count is not contained in the set of at least one predetermined number (step 2100), for example, if Pixel.sub.count is 7 and the set of at least one predetermined number includes 1, 2, 4, and 8, Pixel.sub.sum.sub.--.sub.new will not be determined and the method 2000 continues by determining if all pixels in group g have been assessed (step 2130). It should be appreciated that if Pixel.sub.count is not in the set of at lest one predetermined number, then Pixel.sub.sum.sub.--.sub.new will not include the current value for Pixel.sub.sum. In other words, Pixel.sub.sum.sub.--.sub.new is only set when Pixel.sub.count is within the set of at least one predetermined number.

[0033] Then the method 2000 determines if all pixels in group g have been assessed (step 2130). If not, then the method returns to step 2060 and selects a next pixel n. If all of the pixels in group g have been assessed (step 2130), the method 2000 determines if all groups g have been assessed (step 2140). If all groups g have not been assessed, the method 2000 continues at step 2050 and selects a next group g. If all groups g have been assessed, then p.sub.sig.sub.--.sub.new is determined by dividing Pixel.sub.sum.sub.--.sub.new by Pixel.sub.count.sub.--.sub.new (step 2150). The method 2000 can then be repeated for a next target pixel p at step 2010.

[0034] The method 2000 described above may also be implemented and carried out, as discussed above, on target pixel 32b (FIG. 2B) and associated image correction kernel 101b (FIG. 2B). For example, it may be desirable to average both greenred and greenblue pixels together. If target pixel 32b is a greenred pixel, the correction kernel could be selected to include pixels 30, 12, 34, 52, 21, 23, 41, and 43 where pixels 31, 12, 23, and 52 are greenred pixels and pixels 21, 23, 41, and 43 are greenblue pixels.

[0035] The above described embodiments may not provide sufficient denoising to remove spurious noise (i.e., noise greater than 6 standard deviations). Accordingly, embodiments of the invention are better utilized when implemented after the image data has been processed by a filter which will remove spurious noise.

[0036] In addition to the above described embodiments, a program for operating a processor embodying the methods may be stored on a carrier medium which may include RAM, floppy disk, data transmission, compact disk, etc. which can be executed by an associated processor. For example, embodiments may be implemented as a plug-in for existing software applications or may be used on their own. The embodiments are not limited to the carrier mediums specified herein and may be implemented using any carrier medium as known in the art or hereinafter developed.

[0037] FIG. 6 illustrates an example imager 300 having an exemplary CMOS pixel array 240 with which described embodiments may be used. Row lines of the array 240 are selectively activated by a row driver 245 in response to row address decoder 255. A column driver 260 and column address decoder 270 are also included in the imager 300. The imager 300 is operated by the timing and control circuit 250, which controls the address decoders 255, 270. The timing and control circuit 250 also controls the row and column driver circuitry 245, 260.

[0038] A sample and hold circuit 261 associated with the column driver 260 reads a pixel reset signal Vrst and a pixel image signal Vsig for selected pixels of the array 240. A differential signal (Vrst-Vsig) is produced by differential amplifier 262 for each pixel and is digitized by analog-to-digital converter 275 (ADC). The analog-to-digital converter 275 supplies the digitized pixel signals to an image processor circuit 280 which forms and may output a digital image. The method 200 (FIG. 4) and method 2000 (FIG. 5) may be implemented by a processor circuit. For example, the processor circuit may be the image processor circuit 280 which is implemented as a digital logic processor pipeline or as a programmed processor that is capable of performing the method 200 (FIG. 4) or method 2000 (FIG. 5) on the digitized signals from the pixel array 240. Alternatively, the processor circuit may be implemented as a hardwired circuit that processes the analog output of the pixel array and is located between the amplifier 262 and ADC 275 (not shown). Although the imager 300 has been described as a CMOS imager, this is merely one example imager that may be used. Embodiments of the invention may also be used with other imagers having a different readout architecture. While the imager 300 has been shown as a stand-alone imager, it should be appreciated that the embodiments are not so limited. For example, the embodiments may be implemented on a system-on-a-chip or the imager 300 can be coupled to a separate signal processing chip which implements disclosed embodiments. Additionally, raw imaging data can be output from the image processor circuit 280 (which can be implemented in hardware logic, or as a programmed processor or some combination of the two) and stored and denoised elsewhere, for example, in a system as described in relation to FIG. 7 below or in a stand-alone image processing system.

[0039] FIG. 7 shows system 1100, a typical processor system modified to include the imager 300 (FIG. 6) of an embodiment. The system 1100 is an example of a system having digital circuits that could include imagers. Without being limiting, such a system could include a computer system, still or video camera system, scanner, machine vision, video phone, and auto focus system, or other imager systems.

[0040] System 1100, for example a camera system, may comprise a central processing unit (CPU) 1102, such as a microprocessor, that communicates with one or more input/output (I/O) devices 1106 over a bus 1104. Imager 300 also communicates with the CPU 1102 over the bus 1104. The processor-based system 1100 also includes random access memory (RAM) 1110, and can include removable memory 1115, such as flash memory, which also communicates with the CPU 1102 over the bus 1104. The imager 300 may be combined with a processor, such as a CPU, digital signal processor, or microprocessor, with or without memory storage on a single integrated circuit or on a different chip than the processor. As described above, raw image data from the pixel array 240 (FIG. 6) can be output from the imager 300 image processor circuit 280 and stored, for example in the random access memory 1110 or the CPU 1102. Denoising can then be performed on the stored data by the CPU 1102, or can be sent outside the system 1100 and stored and operated on by a stand-alone processor, e.g., a computer, external to system 1100 in accordance with the embodiments described herein.

[0041] While the embodiments have been described in detail in connection with preferred embodiments known at the time, it should be readily understood that the claimed invention is not limited to the disclosed embodiments. Rather, the embodiments can be modified to incorporate any number of variations, alterations, substitutions, or equivalent arrangements not heretofore described. For example, the methods can be used with pixels in other patterns than the described Bayer pattern, and the correction kernels would be adjusted accordingly. While the embodiments are described in connection with a CMOS imager, they can be practiced with other types of imagers. Thus, the claimed invention is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.

Claims

1. A method of processing an image, comprising the steps of: selecting a set of pixel signals from pixels surrounding an identified target pixel having a target pixel signal; for each of the surrounding pixel signals of the set, determining a respective difference value between the target pixel signal and the surrounding pixel signal; for each of the surrounding pixel signals of the set, determining if the respective difference value is less than a predetermined threshold; and substituting an average value as a value for the target pixel signal, the average value is based on the target pixel signal and at least a selected subset of the pixel signals having a respective difference value less than a predetermined threshold.

2. The method of claim 1, wherein the average value is an average of 2.sup.n pixel signals where n is an integer.

3. The method of claim 1, wherein the target pixel is one of a red, greenred, greenblue, or blue pixel and each of the pixels in the selected set are the same color as the target pixel.

4. The method of claim 1, wherein at least one of the surrounding pixels has been previously denoised.

5. The method of claim 1, wherein the predetermined threshold is based on at least one of an analog and digital gain used in image capture.

6. The method of claim 1, wherein the predetermined threshold is based on the color of the target pixel.

7. A method of processing an image comprising the steps of: selecting a target pixel having a first signal value; replacing a first register value with a sum of the first register value and the first signal value; incrementing a first counter; selecting a correction kernel having a number of pixels surrounding the target pixel, each of the kernel pixels having a respective second signal value; grouping the selected kernel pixels into at least one pixel group; comparing a difference between the first signal value and a one of the respective second signal values to a threshold value; replacing the first register value with a sum of the first register value and one of the respective second signal values based on a result of the comparing the difference step; incrementing the first counter based on a result of the comparing the difference step; comparing a value of the first counter to a set of at least one predetermined number; replacing a second register value with the first register value based on a result of the comparing the value step; replacing a value of a second counter with the value of the first counter based on a result of the comparing the value step; and replacing the first signal value with the result of a division of the second register value by the second counter.

8. The method of claim 7, further comprising repeating the comparing the difference step through replacing the value of the second counter step for each of the respective second signal values.

9. The method of claim 7, further comprising repeating the comparing the difference step through replacing the value of the second counter step for each of the respective second signal values in a first pixel group before repeating the comparing the difference step through replacing the value of the second counter step for each of the respective second signal values in a second pixel group.

10. The method of claim 7, wherein each selected kernel pixel has a respective distance from the target pixel and are grouped based on each kernel pixels' distance from the target pixel.

11. The method of claim 7, wherein the set of at least one predetermined number is comprised of integer powers of the number two.

12. The method of claim 7, wherein the target pixel is one of a red, green, or blue pixel and each of the selected kernel pixels are the same color as the target pixel.

13. A method of processing an image, comprising the steps of: selecting a target pixel having a signal value; selecting a correction kernel associated with the target pixel containing a set of correction kernel pixels each having a respective pixel signal and a respective distance from the target pixel; for each of the correction kernel pixel signals, determining a respective difference value between the target pixel signal and the correction kernel pixel signal; for each of the correction kernel pixel signals, determining if the respective difference value is less than a predetermined threshold; and substituting an average value as a value for the target pixel signal, the average value being an average of 2.sup.n pixel signals where n is an integer, wherein the 2.sup.n signals are comprised of the target pixel signal and at least a subset of the correction kernel pixel signal having a respective difference value less than a predetermined threshold.

14. The method of claim 13, wherein the at least a subset of correction kernel pixel signals is selected based on the correction kernel pixel signals respective distances from the target pixel.

15. An imager comprising: a pixel array for capturing an image and comprising a plurality of pixels, each pixel outputting a signal representing an amount of light received; and a circuit for denoising an image captured by the array, the circuit being configured to: select from the captured image a target pixel having a signal value; select a correction kernel associated with the target pixel containing a set of correction kernel pixels each having a respective pixel signal and a respective distance from the target pixel; for each of the correction kernel pixel signals, determine a respective difference value between the target pixel signal and the correction kernel pixel signal; for each of the correction kernel pixel signals, determine if the respective difference value is less than a predetermined threshold; and substitute an average value as a value for the target pixel signal, the average value being an average of 2.sup.n pixel signals where n is an integer, wherein the 2.sup.n signals are comprised of the target pixel signal and at least a subset of the correction kernel pixel signals having a respective difference value less than a predetermined threshold.

16. The imager of claim 15, wherein the at least a subset of correction kernel pixel signals is selected based on the correction kernel pixel signals respective distances from the target pixel.

17. The imager of claim 15, wherein the imager is part of a camera system.

18. An imager comprising: a pixel array for capturing an image and comprising a plurality of pixels, each pixel outputting a signal representing an amount of light received; and a processing circuit for denoising an image captured by the array, the processing circuit being configured to: select from the captured image a set of pixel signals from pixels surrounding an identified target pixel having a target pixel signal; for each of the surrounding pixel signals of the set, determine a respective difference value between the target pixel signal and the surrounding pixel signal; for each of the surrounding pixel signals of the set, determine if the respective difference value is less than a predetermined threshold; and substitute an average value as a value for the target pixel signal, the average value is based on the target pixel signal and at least a selected subset of the pixel signals having a respective difference value less than a predetermined threshold.

19. The imager of claim 18, wherein the average value is an average of 2.sup.n pixel signals where n is an integer.

20. The imager of claim 18, wherein the target pixel and each pixel in the selected set are the same color.

21. The imager of claim 18, wherein at least one of surrounding pixels has been previously denoised.

22. The imager of claim 18, wherein the imager is part of a camera system.

23. A storage medium containing a program for execution by a processor, the processor when executing the program, performs the steps of: selecting a set of pixel signals from pixels surrounding an identified target pixel having a target pixel signal; for each of the surrounding pixel signals of the set, determining a respective difference value between the target pixel signal and the surrounding pixel signal; for each of the surrounding pixel signals of the set, determining if the respective difference value is less than a predetermined threshold; and substituting an average value as a value for the target pixel signal, the average value is based on the target pixel signal and at least a selected subset of the pixel signals having a respective difference value less than a predetermined threshold.