CN111784603A - RAW domain image denoising method, computer device and computer readable storage medium - Google Patents
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Abstract
The invention provides a RAW domain image denoising method, a computer device and a computer readable storage medium, wherein the method comprises the steps of obtaining an initial image, calculating the brightness value of each pixel according to the chromatic value of each pixel of the initial image, and obtaining an initial brightness image; extracting a chromaticity subgraph and a corresponding brightness subgraph of each color of the initial image; guiding and filtering each chromaticity subgraph by taking the brightness subgraph as a guide graph to obtain a primary denoising brightness subgraph and a primary denoising chromaticity subgraph; performing combined filtering on the primary denoising brightness subgraph and the corresponding primary denoising chroma subgraph to obtain a secondary denoising chroma subgraph; and calculating the output chroma value of each pixel by using the chroma value of each pixel of the secondary denoising chroma subgraph, and performing inverse interpolation calculation on a plurality of chroma subgraphs based on the output chroma value of each pixel to obtain an output image. The invention also provides a computer device and a computer readable storage medium for realizing the method. The invention can improve the quality of the de-noised image.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a RAW domain image denoising method, a computer device for realizing the method and a computer readable storage medium.
Background
Many existing intelligent electronic devices have an image shooting function, for example, a smartphone, a tablet computer, a vehicle data recorder, and the like are provided with a camera device, and the camera device is usually provided with a CMOS sensor to acquire an image. Generally, an image includes a large number of pixels, and color information of each pixel may be represented by RGB values or YUV values.
At present, the image format directly acquired by a common image sensor, such as a CCD sensor and a CMOS sensor, is called RAW format (or Bayer format), and an output image needs to be converted into a color domain image format (RGB or YUV format) for output after being processed by an Image Signal Processor (ISP). Image sensors are susceptible to various factors during image acquisition and transmission to generate noise, such that images directly acquired by the image sensor are typically noisy images. Since the noise signal is mixed with the image signal, there are problems of insignificant image characteristics and low definition, so that the noise reduction processing is usually required to improve the signal-to-noise ratio of the image.
Image noise can be generally divided into luminance noise and color noise, and from the aspect of frequency, luminance noise is generally high-frequency noise, and color noise is generally low-frequency noise. Color noise is particularly noticeable in low-luminance environments, since the human eye is more sensitive to color noise than luminance noise.
Because the Bayer image in the RAW domain is directly acquired by the image sensor and then output, each pixel unit only contains data of one of three primary colors of red, green and blue, and after the Bayer image is processed by the image signal processor, a series of operations such as demosaicing, automatic exposure, dark corner removal and the like can change the original noise characteristics, so that the denoising process becomes more complicated. Therefore, in the prior art, the brightness denoising is usually performed in the RAW domain first, and then the color denoising is performed in the color domain, but the following problems usually exist: firstly, the color noise is removed, and simultaneously, the brightness information is influenced, so that the brightness information is blurred; second, color aliasing/color cast/or reduced image saturation can occur; third, color noise cannot be removed in the RAW domain, resulting in a problem that color noise is amplified after being processed by the image signal processor.
For example, in the prior art, a chroma subgraph of each color is extracted from an original image according to the color of each pixel, and denoising calculation is performed on the chroma subgraph, but the scheme does not consider the difference between luminance noise and color noise, so that the luminance noise is affected while the color noise is removed, and the luminance information is easily blurred. In addition, in the prior art, the denoising processing of the image is usually performed by first-order filtering, and structural noise is easily generated.
Disclosure of Invention
The invention mainly aims to provide a RAW domain image denoising method which can remove brightness noise and color noise and avoid generating structural noise.
The invention also aims to provide a computer device for realizing the method for denoising the RAW domain image.
Still another object of the present invention is to provide a computer readable storage medium for implementing the above-mentioned method for denoising an image in a RAW domain.
In order to achieve the main purpose of the invention, the RAW domain image denoising method provided by the invention comprises the steps of obtaining an initial image, calculating the brightness value of each pixel according to the chromatic value of each pixel of the initial image, and obtaining an initial brightness image; extracting a chromaticity subgraph and a corresponding brightness subgraph of each color of the initial image; guiding and filtering each chromaticity subgraph by taking the brightness subgraph as a guide graph to obtain a primary denoising brightness subgraph and a primary denoising chromaticity subgraph; performing combined filtering on the primary denoising brightness subgraph and the corresponding primary denoising chroma subgraph to obtain a secondary denoising chroma subgraph; and calculating the output chroma value of each pixel by using the chroma value of each pixel of the secondary denoising chroma subgraph, and performing inverse interpolation calculation on a plurality of chroma subgraphs based on the output chroma value of each pixel to obtain an output image.
According to the scheme, the luminance subgraphs are taken as the guide graphs to conduct guide filtering on the various chrominance subgraphs, the guide filtering is mainly used for removing luminance noise in the initial image, and the influence on the removal of the luminance noise caused by the interference of the luminance noise when the color noise is removed subsequently is avoided. Moreover, by performing channel division processing on each color, mutual interference of noise of each color in the denoising process can be avoided, color aliasing or color cast generation is avoided, and the problem of structural noise can also be avoided. In addition, the first-stage filtering is guiding filtering and the second-stage filtering is combined filtering, the luminance sub-image and the chrominance sub-image are used as references for combined filtering, and luminance noise is removed during guiding filtering, so that serious color cast caused by overlarge luminance noise can be avoided during color noise filtering during combined filtering, and most color noise can be removed.
In a preferred embodiment, after obtaining the primary denoised luminance subgraph and the primary denoised chrominance subgraph, the following steps are further performed: performing down-sampling interpolation calculation on the primary denoised luminance subgraph and the primary denoised chrominance subgraph; performing joint filtering on the primary denoised luminance subgraph and the corresponding primary denoised chroma subgraph comprises the following steps: and performing combined filtering by using the down-sampled primary denoised luminance subgraph and the primary denoised chrominance subgraph.
Therefore, the high-frequency characteristics of the primary denoised luminance subgraph and the primary denoised chroma subgraph can be further filtered by performing downsampling interpolation calculation on the primary denoised luminance subgraph and the primary denoised chroma subgraph, and because the luminance noise is mainly the high-frequency characteristics, the luminance noise can be further filtered by downsampling, and the interference of the removal of the color noise caused by the existence of the luminance noise during the color noise filtering is avoided.
Further, the step of obtaining the secondary denoising chroma subgraph comprises: and performing up-sampling interpolation calculation on the image subjected to the joint filtering by applying the down-sampled primary denoising luminance subgraph and the primary denoising chrominance subgraph to obtain a secondary denoising chrominance subgraph.
Therefore, the total number of the pixel points of the secondary denoising chromaticity subgraph and the primary denoising chromaticity subgraph can be ensured to be equal after upsampling, and the calculation of the output chromaticity value of each pixel is facilitated.
Further, the calculating the output chroma value of each pixel by using the chroma value of each pixel of the secondary denoised chroma sub-graph comprises: and calculating the output colorimetric value of each pixel by using the colorimetric values of each pixel of the secondary denoising chroma subgraph, the primary denoising chroma subgraph and the initial image.
More preferably, calculating the output chroma value of each pixel by using the chroma value of each pixel of the secondarily denoised chroma sub-graph comprises: and performing fusion calculation on the chroma values of the pixels of the secondary denoising chroma subgraph, the primary denoising chroma subgraph and the initial image according to a preset weight value to obtain the output chroma value of each pixel.
Therefore, the output colorimetric values of the pixels are calculated according to the colorimetric values of the pixels of the secondary denoising chromaticity subgraph, the primary denoising chromaticity subgraph and the initial image, so that the calculated output colorimetric values of the pixels are closer to the real color, and the denoised image color is more vivid.
Preferably, the performing guided filtering on each chrominance sub-image by using the luminance sub-image as a guide image includes: acquiring a first brightness search window of the brightness subgraph and a first chrominance search window of each chrominance subgraph, traversing each pixel of the first brightness search window and performing matching calculation of a matching window on each pixel, calculating an average value of brightness values of all pixels meeting matching requirements in the first brightness search window as a guide filtering brightness value of a current pixel, and calculating an average value of chrominance values of all pixels meeting matching requirements in the first chrominance search window as a guide filtering chrominance value of the current pixel; and meeting the matching requirement that the brightness value of the pixel in the matching window meets the preset brightness threshold value requirement.
Therefore, in the process of guiding and filtering each chrominance subgraph by taking the luminance subgraph as the guiding graph, only whether the luminance value of the pixel in the matching window meets the requirement of the preset luminance threshold value is judged, namely only the luminance information is taken as the main parameter of the filter, so that the image after guiding and filtering can keep the color noise, the initial denoising chrominance subgraph used in the joint filtering still keeps the color noise, and the color noise is prevented from being removed when the luminance noise is removed.
Further, the step of matching the brightness values of the pixels in the window to meet the requirement of the preset brightness threshold includes: and the preset error arithmetic value of the brightness value of each pixel in the matching window is smaller than the preset brightness threshold value.
Therefore, whether the current matching window meets the preset requirement or not is determined through the preset brightness threshold, the calculated amount is reduced, when the algorithm is realized through a hardware circuit, the hardware circuit is simple, and the realization cost of the algorithm is reduced.
Further, the step of jointly filtering the primary denoised luminance subgraph and the corresponding primary denoised chrominance subgraph comprises: acquiring a second brightness search window of the primary denoising brightness subgraph and a second chrominance search window of each primary denoising chromaticity subgraph, traversing each pixel of the second brightness search window and the corresponding second chrominance search window, performing matching calculation of the matching windows on each pixel, calculating an average value of brightness values of all pixels meeting the matching requirements in the second brightness search window and the corresponding second chrominance search window as a joint filtering brightness value of the current pixel, and calculating an average value of chroma values of all pixels meeting the matching requirements in the second brightness search window and the corresponding second chrominance search window as a joint filtering chroma value of the current pixel; wherein, satisfying the matching requirement is: the brightness values of the pixels in the matching window meet the preset brightness threshold requirement, and the chroma values of the pixels in the matching window meet the preset chroma threshold requirement.
It can be seen that, in the process of the joint filtering, the luminance value and the chrominance value of each pixel are considered at the same time, that is, the luminance value and the chrominance value are used as the main parameters of the filter together for filtering, so that the luminance noise and the color noise of each pixel can be filtered at the same time. Since the luminance noise is already filtered during the pilot filtering, the influence of the luminance noise on the color noise can be avoided during the joint filtering.
In order to achieve the above another object, the present invention provides a computer device including a processor and a memory, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the steps of the RAW domain image denoising method.
To achieve the above-mentioned further object, the present invention provides a computer program stored on a computer readable storage medium, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned RAW domain image denoising method.
Drawings
FIG. 1 is a flowchart of an embodiment of a RAW domain image denoising method according to the present invention.
Fig. 2 is a schematic diagram of the arrangement of chrominance values of pixels of an initial image.
FIG. 3 is a schematic diagram of a plurality of neighborhood windows in an initial image.
Fig. 4 is a schematic diagram of extracting a plurality of chroma subgraphs from an initial image.
FIG. 5 is a schematic diagram of extracting multiple luminance sub-images from an initial image.
Fig. 6 is a schematic diagram of a search window and a matching window.
FIG. 7 is a schematic diagram of downsampling in an embodiment of the RAW domain image denoising method of the present invention.
The invention is further explained with reference to the drawings and the embodiments.
Detailed Description
The RAW domain image denoising method is applied to intelligent electronic equipment, preferably, the intelligent electronic equipment is provided with a camera device such as a camera and the like, the camera device is provided with an image sensor such as a CMOS and the like, the intelligent electronic equipment acquires an initial image by using the camera device, and the method is a method for processing color noise and brightness noise of the initial image acquired by the image sensor. Preferably, the intelligent electronic device is provided with a processor and a memory, the memory stores a computer program, and the processor implements the above-mentioned RAW domain image denoising method by executing the computer program.
The embodiment of the RAW domain image denoising method comprises the following steps:
in this embodiment, mainly, a denoising method is performed on an initial image obtained by an image sensor, referring to fig. 1, step S1 is first executed to obtain an initial image, specifically, an initial image output by a CCD sensor or a CMOS sensor is obtained. Generally, the color information of the initial image is RGB information. Taking a Bayer image as an example, as shown in fig. 2, the Bayer image format includes a large number of pixels, each of which has color information, for example, the color information of the first row of pixels is color information of red R or green Gr, the red R pixels and the green Gr pixels are arranged at intervals, the color information of the second row of pixels is color information of green Gb or blue B, and the green Gb pixels and the blue B pixels are arranged at intervals. The color information for each pixel is a chrominance value, which is typically a binary number from 0 to 255.
Then, step S2 is executed to calculate the luminance value of each pixel and form an initial luminance map. Specifically, for each pixel, interpolation calculation is performed on the 3 × 3 pixels in the neighborhood of the pixel. As shown in fig. 3, if the central pixel of a certain pixel is red R, the structure of the neighborhood 3 × 3 pixels of the certain pixel is as shown in fig. 3(a), and the luminance value of the red R pixel can be obtained by the following formula:
y ═ 4 xr +2 x Σ G + Σb)/16 (formula 1)
If the central pixel of a certain pixel is blue B, the structure of the neighborhood 3 × 3 pixels of the pixel is as shown in fig. 3(d), and the luminance value of the blue B pixel can be calculated by the following formula:
y ═ 4 × B +2 × Σ G + Σr)/16 (formula 2)
Accordingly, if the central pixel of a certain pixel is green Gr or Gb, the structure of the neighborhood 3 × 3 pixels of the pixel is as shown in fig. 3(b) or fig. 3(c), and the luminance value of the green Gr or Gb pixel can be calculated by the following formula:
Y＝(4×G0+2 ×∑ R +2 ×∑ B)/8 (formula 3)
In the above formula 1, formula 2, and formula 3, ∑ R is the sum of the chrominance values of all red R pixels in the 3 × 3 neighborhood, ∑ B is the sum of the chrominance values of all green B pixels in the 3 × 3 neighborhood, ∑ G is the sum of the chrominance values of all green Gr and Gb pixels in the 3 × 3 neighborhood, and G is the sum of the chrominance values of all green Gr and Gb pixels in the 3 × neighborhood0When the central pixel is a green Gr or green Gb pixel, the chromaticity value of the central pixel is determined.
After calculating the brightness value of each pixel, each pixel in the initial image will have a corresponding brightness value, and each pixel is represented by the brightness value, i.e. an initial brightness map is formed.
Next, step S3 is executed to extract a chroma sub-image of each color and extract a luminance sub-image corresponding to each chroma sub-image. Specifically, all red R pixels in the initial image are extracted to form a chromaticity subgraph of red R pixels, all blue B pixels in the initial image are extracted to form a chromaticity subgraph of blue B pixels, all green Gr pixels in the initial image are extracted to form a chromaticity subgraph of green Gr pixels, all green Gb pixels in the initial image are extracted to form a chromaticity subgraph of green Gb pixels, so as to form four chromaticity subgraphs, and fig. 4 shows the structure of four chromaticity subgraphs.
The extraction of each luminance subgraph is determined according to the color channel of each pixel, and correspondingly, the luminance subgraph corresponding to each color channel is extracted from the initial luminance graph according to the color channel of each pixel, as shown in fig. 5, for a red R pixel, the luminance values of all red R pixels are extracted and form the luminance subgraph of the red R pixel, the luminance values of all blue B pixels are extracted and form the luminance subgraph of a blue B pixel, the luminance values of all green Gr pixels are extracted and form the luminance subgraph of the green Gr pixel, and the luminance values of all green Gb pixels are extracted and form the luminance subgraph of the green Gb pixel, so that four luminance subgraphs are formed. It can be seen that each luminance sub-image of the present embodiment corresponds to a chrominance sub-image, i.e. the chrominance sub-image of the red R pixel corresponds to the luminance sub-image of the red R pixel, and so on.
Since the number of red R pixels in the initial image is 1/4 of the initial image, the width and height of the extracted chrominance sub-image of the red R pixels and the corresponding luminance sub-image of the red R pixels are 1/2 of the width and height of the initial image. Similarly, for the blue B pixels, the green Gr pixels, and the green Gb pixels, the width and height of the extracted chroma subgraph and the luminance subgraph are 1/2 of the width and height of the original image.
Then, step S4 is executed, and the luminance subgraph is used as a guide graph to guide and filter the corresponding chroma subgraph, so as to obtain a primary denoised luminance subgraph and a primary denoised chroma subgraph. In this embodiment, when the guided filtering is to perform filtering and noise reduction by using a guide image, the calculated filter parameters are directly used to guide the corresponding chroma sub-image to perform filtering operation, and when the filter parameters are calculated, only data of the luminance sub-image is used, that is, the luminance value of each pixel in the luminance sub-image is used as a reference for filtering, so that the calculated primary de-noised luminance sub-image and the primary de-noised chroma sub-image are images de-noised for luminance, that is, the guided filtering is mainly to remove luminance noise in the images, color noise in the images is retained as much as possible, and the color noise is filtered in subsequent combined filtering.
The specific steps of guided filtering are described in detail below. The guided filtering in this embodiment is performed for each pixel of the luminance sub-image and the chrominance sub-image, that is, for each pixel, the chrominance value and the luminance value after the initial denoising need to be calculated. Firstly, regarding a pixel to be calculated, taking a current pixel as a center, extracting a search window with the neighborhood size of 9 × 9, and if the search window is extracted under a luminance subgraph, the search window is a first luminance search window. Fig. 6 shows a first luminance search window 10 of size 9 x 9 extracted from the luminance subgraph.
Then, a matching window 11 with a size of 3 × 3 centered on the current pixel is set, then, all pixels in the search window 10 are traversed, a neighborhood window (the size of the neighborhood window is also 3 × 3) of each pixel in the search window 10 is taken as a matching window, for example, the matching window 12, the central pixel of the matching window 12 is a pixel to be matched, and the matching window 12 of each pixel and the matching window 11 of the current pixel are subjected to matching operation. The matching operation of the present embodiment is performed using a sum of absolute difference algorithm (SAD algorithm). Specifically, the absolute value of the difference between the brightness value of each pixel in the matching window 11 of the current pixel and the brightness value of the corresponding pixel in the matching window 12 is calculated, for example, the absolute value of the difference between the brightness value of the pixel at the upper left corner of the matching window 11 and the brightness value of the pixel at the upper left corner of the matching window 12 is calculated, the absolute values of the differences between the brightness values of the other eight pixels are sequentially calculated, then the sum of the absolute values of the brightness values of the nine pixels is calculated, whether the sum of the absolute values of the brightness values of the nine pixels is greater than a preset brightness threshold is judged, if the sum of the absolute values of the brightness values of the nine pixels is not greater than the preset brightness threshold, the central pixel of the matching window 12 is considered to be similar to the central pixel of the matching window 11 of the current pixel, that is, that.
After traversing all pixels of the current search window 10, determining all pixels to be matched which are similar to the current pixel, accumulating the brightness values of all similar pixels to be matched, calculating the average value of the brightness values of all similar pixels to be matched, and taking the average value as the brightness value of the current pixel after primary filtering. After the brightness value of each pixel after primary filtering is calculated, a brightness subgraph after primary denoising can be obtained.
And extracting a first chroma search window aiming at the chroma subgraph, wherein each pixel of the first chroma search window and each pixel of the first brightness search window are in one-to-one correspondence. Then, after all pixels to be matched similar to the current pixel are determined, the average value of the chroma values of all similar pixels to be matched is calculated, and the average value of the chroma values is used as the chroma value of the current pixel after primary filtering. After the chroma value of each pixel after primary filtering is calculated, a chroma subgraph after primary denoising can be obtained. It should be noted that the criterion for determining whether a certain pixel is similar to the current pixel is that the sum of absolute values of differences between luminance values of nine pixels in the matching window is not greater than a preset luminance threshold, that is, the criterion for determining is based on the luminance value rather than on the chrominance value, therefore, the guided filtering is performed by using the luminance subgraph as a guide map, and the parameters of the filter are related to the luminance value rather than the chrominance value of the pixel. Thus, after the guiding filtering is performed, most of the luminance noise in the image can be removed, that is, a denoising operation of the luminance noise is performed once.
Through the operation, four primary denoising chroma subgraphs of four colors and four corresponding primary denoising chroma subgraphs can be obtained. Then, step S5 is executed to perform downsampling interpolation calculation on each primary denoised chroma sub-image and its corresponding primary denoised luma sub-image. Specifically, the downsampling calculation may be implemented by a Binning method, which is a schematic diagram of 2-fold downsampling interpolation calculation as shown in fig. 7, and the image is sampled at intervals of 2 pixels, that is, the average value of adjacent 2 × 2 pixels is obtained to obtain a downsampled value, for example, for a 2 × 2 pixel window 15, the average value of chrominance values of four pixels in the window 15 is obtained as the chrominance value of the downsampled pixel 16, and the average value of luminance values of four pixels in the window 15 is obtained as the luminance value of the downsampled pixel 16.
Thus, after 2 times of downsampling interpolation calculation is carried out on the primary denoised chroma subgraph, the resolution of the obtained downsampled image is 1/2 of the primary denoised chroma subgraph; similarly, if 4 times of downsampling interpolation calculation is performed, the resolution of the obtained image is 1/4 of the primary denoised chroma subgraph. The downsampling interpolation calculation of the luminance subgraph is also to filter the luminance values of the pixels, namely, the average value of the luminance values of the four pixels is used for replacing the luminance values of the four pixels.
And then, executing step S6, and performing joint filtering on each down-sampled channel chroma subgraph and the corresponding luminance subgraph to obtain a secondary denoising chroma subgraph. In this embodiment, the calculation of the joint filtering is similar to the calculation of the guided filtering, but the difference is that when calculating the filter coefficients, the calculation is performed according to the luminance value and the chrominance value of each pixel, instead of being performed only by relying on the data of the luminance value.
The specific steps of the joint filtering are described in detail below. Firstly, regarding a pixel to be calculated, taking a current pixel as a center, extracting a search window with neighborhood size of 9 × 9 from a downsampled primary denoised luminance subgraph, wherein the search window is a second luminance search window, and extracting a search window with neighborhood size of 9 × 9 from the downsampled primary denoised chrominance subgraph, wherein the search window is a second chrominance search window.
Then, for the second brightness search window, a matching window with a size of 3 × 3 with the current pixel as the center is set, then, all pixels in the second brightness search window are traversed, a neighborhood window (the size of the neighborhood window is also 3 × 3) of each pixel in the second brightness search window is taken as a matching window of the pixel to be matched, and then, matching operation is performed on the matching window of the current pixel and the matching window of the pixel to be matched, for example, matching operation of an absolute error sum algorithm (SAD algorithm) is performed, and matching operation of the absolute error sum algorithm is not repeated.
Then, a matching window with the size of 3 × 3 with the current pixel as the center is set for the second chrominance search window, then, all pixels in the second chrominance search window are traversed, a neighborhood window (the size of the neighborhood window is also 3 × 3) of each pixel in the second chrominance search window is taken as a matching window of the pixel to be matched, and then, the matching window of the current pixel and the matching window of the pixel to be matched are subjected to matching operation, for example, matching operation of a sum of absolute differences (SAD algorithm). When the matching operation of the absolute error sum algorithm is performed for the second chroma search window, the chroma value, not the luma value, of each pixel is used as the data.
In the joint filtering calculation, the factors of the luminance value and the chrominance value need to be considered at the same time, that is, a certain pixel to be matched is confirmed to be similar to the current pixel, and the following conditions need to be satisfied: and under the down-sampled primary denoising luminance subgraph, the matching operation result of the matching window of the current pixel and the matching window of the pixel to be matched is not larger than a luminance threshold value, and under the down-sampled primary denoising chrominance subgraph, the matching operation result of the matching window of the current pixel and the matching window of the pixel to be matched is not larger than a chrominance threshold value. If a certain pixel to be matched meets the condition, the pixel to be matched is considered to be similar to the current pixel, otherwise, the pixel to be matched is not considered to be similar to the current pixel.
After traversing all pixels of the second brightness search window and the second chrominance search window, determining all pixels to be matched which are similar to the current pixel, accumulating the chrominance values of all the similar pixels to be matched, calculating the average value of the chrominance values of all the similar pixels to be matched, and taking the average value as the chrominance value of the current pixel after secondary filtering. After the chroma value of each pixel after secondary filtering is calculated, a chroma subgraph after secondary denoising can be obtained.
In the process of the combined filtering and noise reduction calculation, the parameter design of the filter refers to the data of the brightness value and the chromatic value at the same time, the main brightness noise is removed before the combined filtering and noise reduction, and the downsampling interpolation calculation is carried out on the brightness subgraph, so most of color noise can be removed through the combined filtering and noise reduction, and the interference of the brightness noise on the color noise is very little.
Then, step S7 is executed to perform upsampling interpolation calculation on the secondary denoised chroma subgraph obtained by the joint filtering. Because the down-sampling interpolation calculation is performed on the primary denoised chroma subgraph in the step S5, the up-sampling interpolation calculation is performed according to the same multiplying power in the step S7, and after the up-sampling interpolation calculation, the pixels of the obtained secondary denoised chroma subgraph are the same as those of the primary denoised chroma subgraph. In this embodiment, the upsampling interpolation calculation may have a plurality of methods, such as bilinear interpolation, bicubic interpolation, and Cubic interpolation algorithm, which is not limited in this embodiment.
Then, step S8 is executed, the chroma value of each pixel of the secondary denoised chroma sub-image calculated by the upsampling interpolation is used to calculate the output chroma value of each pixel, specifically, the embodiment uses the chroma values of each pixel in the upsampled secondary denoised chroma sub-image, the primary denoised chroma sub-image and the initial image to perform fusion calculation according to a certain weight, and obtains the output chroma value of each pixel, so as to keep the original image details as much as possible and reduce the influence of the noise reduction algorithm on the initial image. Specifically, the fusion calculation is a weighted fusion calculation of the two filtering results and the initial image according to a set fusion weight, and for example, the following formula is used for calculation:
Pout＝w2×Pfilt2+(1-w2)×(w1×Pfilt1+(1-w1)×Pori) (formula 4)
Wherein, PoutIs the output chrominance value, w, of a pixel1For the weights of the primary denoised chroma subgraph, w2For secondary denoising of the weights, P, of the chrominance subgraphsfilt1For the chroma value, P, of the pixel in the primary denoised chroma sub-imagefilt2For up-sampled twice-denoised chroma subgraphThe chrominance value, P, of the pixeloriIs the chrominance value of the pixel in the initial image. It is understood that the above formula is an exemplary formula, and other variations of the above formula may be used in practice, and the embodiment is not limited.
Finally, step S9 is executed to perform inverse interpolation calculation according to the multiple chroma subgraphs and output the image subjected to denoising calculation. Since step S3 is to extract the chroma sub-images of each color channel from the original image, i.e., to divide the original image into four chroma sub-images according to the color of each pixel, step S9 is to restore the positions of the pixels in the original image in the reverse process of step S3 according to the Bayer pattern of the original image, so as to form an output image, and the chroma value of each pixel of the output image is the output chroma value calculated in step S8.
The invention firstly extracts the chromaticity subgraph and the corresponding brightness subgraph of each color channel according to the color of each pixel, and conducts guide filtering and combined filtering, so that the chromaticity values and the brightness values of the pixels with different colors in the filtering process can not interfere with each other, the situations of color aliasing and color cast generation of the filtered image are avoided, the problem of structural noise can also be avoided, and the quality of the filtered image is improved.
In addition, because the filtering of the brightness noise is firstly carried out once before the filtering of the color noise, the invention carries out two-stage filtering, compared with the prior art which only adopts a one-stage filtering mode, the invention can avoid the problem of fuzzy brightness noise information, and the image denoising effect is better. In addition, the calculation amount of the invention is small, when the algorithm of the invention is realized by hardware, the hardware circuit is not complex, and the realization cost of the invention can be reduced.
Of course, the above-mentioned embodiments are the preferred embodiments of the present invention, and in the practical application, the following changes may be made:
in the process of guided filtering and combined filtering noise reduction, the matching algorithm used is not limited to use of sum of absolute differences (MAD) algorithm to calculate pixel similarity, and may also be calculated in other ways to achieve similar effects, such as using Mean Absolute Difference (MAD), Sum of Absolute Differences (SAD), sum of squared errors (SSD), sum of squared average errors (MSD), normalized product correlation (NCC), Sequential Similarity Detection (SSDA), hadamard transform (SATD), and so on.
In addition, in the processes of guiding filtering and combined filtering and noise reduction, the mean filter is not limited to be used for filtering similar pixels, and other filtering and noise reduction algorithms can be adopted to achieve similar effects, such as non-local mean (NLM), bilateral filtering and Gaussian filtering methods. In addition, the filtering and noise reduction process is not limited to the filtering and noise reduction through the similarity pixels, and similar effects can be achieved by adopting the guide filtering or the frequency domain filtering.
Finally, the size of the filter window for the pilot filtering and the joint filtering may also be adjusted, and the above embodiment uses 9 × 9 neighborhood as the search window and 3 × 3 neighborhood as the matching window, but similar effects may be achieved with other sizes, for example, 5 × 5, 7 × 7, 11 × 11 neighborhood as the search window and 5 × 5, 7 × 7 neighborhood as the matching window.
The embodiment of the computer device comprises:
the computer device of this embodiment may be an intelligent electronic device, and the computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the steps of the RAW domain image denoising method. Of course, the intelligent electronic device further includes a camera device for acquiring an initial image.
For example, a computer program may be partitioned into one or more modules that are stored in a memory and executed by a processor to implement the modules of the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the terminal device and connecting the various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Computer-readable storage medium embodiments:
the computer program stored in the computer device may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow of the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the above-mentioned RAW domain image denoising method.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
Finally, it should be emphasized that the present invention is not limited to the above-described embodiments, such as the variation of the filtering template, or the variation of the specific algorithm for performing the mean filtering, and such variations should also be included in the protection scope of the present invention.
Claims (10)
1. A RAW domain image denoising method comprises the following steps:
acquiring an initial image, and calculating the brightness value of each pixel according to the chromatic value of each pixel of the initial image to acquire an initial brightness image;
extracting a chromaticity subgraph and a corresponding brightness subgraph of each color of the initial image;
the method is characterized in that:
guiding and filtering each chroma subgraph by taking the brightness subgraph as a guide graph to obtain a primary de-noised brightness subgraph and a primary de-noised chroma subgraph;
performing joint filtering on the primary denoising brightness subgraph and the corresponding primary denoising chroma subgraph to obtain a secondary denoising chroma subgraph;
and calculating the output chroma value of each pixel by using the chroma value of each pixel of the secondary denoising chroma subgraph, and performing inverse interpolation calculation on a plurality of chroma subgraphs based on the output chroma value of each pixel to obtain an output image.
2. The RAW domain image denoising method of claim 1, wherein:
after the primary denoising brightness subgraph and the primary denoising chroma subgraph are obtained, the following steps are also executed: performing downsampling interpolation calculation on the primary denoised luminance subgraph and the primary denoised chrominance subgraph;
jointly filtering the primary denoised luminance subgraph and the corresponding primary denoised chroma subgraph comprises: and performing combined filtering by using the down-sampled primary denoised luminance subgraph and the primary denoised chroma subgraph.
3. The RAW domain image denoising method of claim 2, wherein:
the obtaining of the secondary denoising chroma subgraph comprises the following steps: and performing up-sampling interpolation calculation on the image which is subjected to the joint filtering by using the down-sampled primary denoising luminance subgraph and the primary denoising chrominance subgraph to obtain the secondary denoising chrominance subgraph.
4. The RAW domain image denoising method according to any one of claims 1 to 3, wherein:
calculating an output chroma value of each pixel by applying each pixel chroma value of the secondary denoised chroma sub-graph comprises: and calculating the output chroma value of each pixel by applying the secondary denoising chroma subgraph, the primary denoising chroma subgraph and the chroma value of each pixel of the initial image.
5. The RAW domain image denoising method of claim 4, wherein:
calculating an output chroma value of each pixel by applying each pixel chroma value of the secondary denoised chroma sub-graph comprises: and performing fusion calculation on the chroma values of the pixels of the secondary denoising chroma subgraph, the primary denoising chroma subgraph and the initial image according to a preset weight value to obtain the output chroma value of each pixel.
6. The RAW domain image denoising method according to any one of claims 1 to 3, wherein:
the guiding filtering of each chroma subgraph by taking the luma subgraph as a guiding graph comprises the following steps:
acquiring a first brightness search window of the brightness subgraph and a first chroma search window of each chroma subgraph, traversing each pixel of the first brightness search window and performing matching calculation of a matching window on each pixel, calculating an average value of brightness values of all pixels meeting matching requirements in the first brightness search window as a guide filtering brightness value of a current pixel, and calculating an average value of chroma values of all pixels meeting matching requirements in the first chroma search window as a guide filtering chroma value of the current pixel;
and meeting the matching requirement that the brightness value of the pixel in the matching window meets the requirement of a preset brightness threshold.
7. The method of denoising a RAW domain image according to claim 6, wherein:
the step of enabling the brightness value of the pixel in the matching window to meet the requirement of a preset brightness threshold comprises the following steps: and the preset error arithmetic value of the brightness value of each pixel in the matching window is smaller than the preset brightness threshold value.
8. The RAW domain image denoising method according to any one of claims 1 to 3, wherein:
jointly filtering the primary denoised luminance subgraph and the corresponding primary denoised chroma subgraph comprises:
acquiring a second brightness search window of the primary denoising brightness subgraph and a second chrominance search window of each primary denoising chromaticity subgraph, traversing each pixel of the second brightness search window and the corresponding second chrominance search window, performing matching calculation of a matching window on each pixel, calculating an average value of brightness values of all pixels meeting matching requirements in the second brightness search window and the corresponding second chrominance search window as a joint filtering brightness value of a current pixel, and calculating an average value of chroma values of all pixels meeting matching requirements in the second brightness search window and the corresponding second chrominance search window as a joint filtering chroma value of the current pixel;
wherein, satisfying the matching requirement is: the brightness value of the pixel in the matching window meets the requirement of a preset brightness threshold, and the chroma value of the pixel in the matching window meets the requirement of a preset chroma threshold.
9. Computer arrangement, characterized in that it comprises a processor and a memory, said memory storing a computer program which, when executed by the processor, carries out the steps of the method for de-noising an image in the RAW domain according to any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implements the steps of the method for de-noising an image in the RAW domain as claimed in any one of claims 1 to 8.
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