CN111161188A - Method for reducing image color noise, computer device and computer readable storage medium - Google Patents
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Abstract
The invention provides a method for reducing image color noise, a computer device and a computer readable storage medium, wherein the method comprises the steps of obtaining an initial image, and calculating the output chromatic value of each pixel of the initial image; wherein calculating the output chrominance value of each pixel of the initial image comprises: acquiring an initial chromatic value of a pixel to be denoised, acquiring an initial denoising region corresponding to the pixel to be denoised, and sampling and storing the pixel in the initial denoising region by a preset multiple to obtain a target denoising region, wherein the preset multiple is an integral multiple greater than 2; and obtaining the chroma value of each pixel in the target denoising area, carrying out mean filtering on the initial chroma value of the pixel to be denoised, and calculating the output chroma value of the pixel to be denoised according to the chroma value after mean filtering. The invention also provides a computer device and a computer readable storage medium for realizing the method. The invention can reduce the data storage amount and the operation amount in the image denoising calculation process.
Description
Technical Field
The present invention relates to the technical field of image processing, and in particular, to a method for reducing image color noise, and a computer device and a computer-readable storage medium for implementing the method.
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.
For example, CMOS image sensors commonly used at present generally adopt a BAYER arrangement format, each pixel has only one color information, i.e. RGB values, but the RGB values are not information of three primary colors, and the image has color distortion, so that each pixel needs to be subjected to demosaic (demosaic) processing to obtain RGB three primary color information to restore the original color of the image.
With the increase of image resolution, the reduction of the photosensitive quantity of a single pixel and the wider application of low-illumination scenes, the image noise output by the CMOS image sensor is greatly increased. In the process of converting the RAW image into the RGB image, the demosaicing operation needs to refer to all image pixels in a certain area to obtain three primary colors of RGB of one pixel, and noise of each color component is diffused mutually in a large range, so that a large block (from several pixels to hundreds of pixels) of color spots appear in the final image, and the appearance of human eyes is seriously affected. Therefore, it is generally necessary to perform a denoising process on an image output from the CMOS image sensor.
The currently and generally used method for reducing color noise is a method of applying mean filtering to a chromatic value of each pixel, the current method is to select a pixel as a pixel to be denoised, and select pixels with a certain number of rows and columns as a denoising region by taking the pixel as a center, for example, select pixels with 33 rows and 65 columns as the denoising region, so that the number of pixels in the denoising region is 2145(33 × 65), since mean filtering calculation needs to be performed, data of the chromatic value of each pixel needs to be stored, and a storage space of 17160 bits is needed to store the chromatic values of all pixels in the denoising region by taking the chromatic value of each pixel as an example of 8 bits. In addition, when performing the mean filtering, 2145 times of mean filtering calculation is required, and the calculation amount is very large.
Obviously, the current color noise reduction method has the following problems: due to the fact that the range of the denoising region is very large, more than dozens of lines of pixels need to be cached for denoising under normal conditions, a large amount of storage resources and calculation resources are occupied, the intelligent electronic device can achieve the operation of reducing the color noise of the image only by being provided with a large memory and a processor with high computing capacity, and the production cost of the intelligent electronic device is high. In addition, when the color noise of the image is filtered, the real color details of the object cannot be ensured at the same time, and the color distortion of the image is easily caused.
Disclosure of Invention
The invention mainly aims to provide a method for reducing image color noise by reducing data storage capacity and calculation amount of an intelligent electronic device.
Another object of the present invention is to provide a computer device for implementing the method for reducing color noise of an image.
It is still another object of the present invention to provide a computer readable storage medium for implementing the above method for reducing color noise of an image.
In order to achieve the main object of the present invention, the method for reducing image color noise provided by the present invention comprises obtaining an initial image, calculating an output chrominance value of each pixel of the initial image; wherein calculating the output chrominance value of each pixel of the initial image comprises: acquiring an initial chromatic value of a pixel to be denoised, acquiring an initial denoising region corresponding to the pixel to be denoised, and sampling and storing the pixel in the initial denoising region by a preset multiple to obtain a target denoising region, wherein the preset multiple is an integral multiple greater than 2; and obtaining the chroma value of each pixel in the target denoising area, carrying out mean filtering on the initial chroma value of the pixel to be denoised, and calculating the output chroma value of the pixel to be denoised according to the chroma value after mean filtering.
According to the scheme, after the initial denoising region is obtained, the initial denoising region is sampled and stored to form the target denoising region, the number of pixels in the target denoising region is greatly reduced, so that the data quantity of pixel chromatic values to be stored is greatly reduced, the calculation quantity is also greatly reduced when mean filtering is carried out, and the requirement on intelligent electronic equipment hardware is reduced. In addition, the initial denoising region is stored in a sampling mode to obtain a target denoising region, and the difference between the chromatic values of adjacent pixels is small, so that the color reality of the denoised image cannot be obviously reduced by the method, and particularly, the image with high resolution cannot cause obvious distortion of the image color.
Preferably, calculating the output chroma value of the pixel to be denoised according to the mean-filtered chroma value comprises: and carrying out preset color protection calculation on the chrominance value subjected to the average filtering to obtain an output chrominance value.
Therefore, the chromatic value after the mean value filtering is subjected to color protection calculation, certain colors after the noise removal can be avoided, for example, the situation that the green saturation of trees is too low or the color distortion is serious occurs, and the color reality of the image after the noise removal is improved.
Further, the color protection meter comprises: and calculating an output colorimetric value by using a preset protection weighted value and the colorimetric value after mean filtering.
Therefore, a protection weight value is preset to calculate the output chromatic value, color protection calculation can be rapidly realized, the image denoising efficiency is improved, and the problem that the image can be displayed for a long time due to the overlong image denoising calculation time is solved.
Further, the sampling and storing the pixels in the initial denoising region by the preset times comprises: and sampling and storing the row number and/or the column number of the pixels in the initial denoising area by preset times.
Therefore, pixels in the initial denoising region are sampled in a row and column mode, pixel sampling storage can be rapidly achieved, and time required by image sampling storage is reduced.
Preferably, the mean filtering of the initial chrominance values of the pixels to be denoised comprises: and calculating a preset distance value between the chromatic value of each pixel in the target denoising area and the initial chromatic value of the pixel to be denoised, and calculating the average value of the chromatic values of all pixels of which the preset distance values are smaller than the noise threshold.
Therefore, the chrominance value condition of the pixel to be denoised can be reflected truly by calculating the sum of the chrominance values of the pixels of which the preset distance value is smaller than the noise threshold value and dividing the sum by the average value obtained by the number of the pixels of which the preset distance value is smaller than the noise threshold value, and the occurrence of the abrupt change of the chrominance values of the pixel to be denoised is effectively avoided.
In a further aspect, the noise threshold is positively correlated to the color noise sensitivity value, which increases with increasing pixel brightness.
It can be seen that a color noise sensitive value is preset, and a noise threshold is calculated through the color noise sensitive value, so that the calculated noise threshold is related to the brightness of the pixel. Since color noise has a certain relationship with the brightness of a pixel, for example, the reason for generating the color noise may be caused by insufficient illumination, the color reality of the denoised image can be improved by calculating the noise threshold value by introducing the color noise sensitive value related to the brightness.
Further, the noise threshold is positively correlated to the characteristic value of the image sensor. Therefore, the set noise threshold can reflect the characteristics of the image sensor, and the image is subjected to targeted denoising according to the characteristics of the image sensor.
Further, the predetermined distance value is one of a simplified euclidean distance, a manhattan distance, a minkoch distance, a chebyshev distance, and a cosine distance.
It can be seen that simplified euclidean distance, manhattan distance, minkoch distance, chebyshev distance, and cosine distance are common distance values for image filtering calculation, and the workload of image denoising calculation can be simplified by using the distance values.
To achieve the above-mentioned another object, the present invention provides a computer device comprising 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 method for reducing color noise of an image.
To achieve the above-mentioned further object, the present invention provides a computer program stored on a computer readable storage medium, which when executed by a processor implements the steps of the above-mentioned method for reducing color noise of an image.
Drawings
FIG. 1 is a flowchart illustrating an embodiment of a method for reducing color noise of an image according to the present invention.
FIG. 2 is a schematic diagram of pixel storage of an initial denoising region in an embodiment of the method for reducing color noise of an image according to the present invention.
FIG. 3 is a schematic diagram of pixel storage of a target denoising region in an embodiment of the method for reducing image color noise according to the present invention.
FIG. 4 is a graph of color noise sensitivity value versus pixel brightness for an embodiment of a method for reducing color noise in an image according to the present invention.
The invention is further explained with reference to the drawings and the embodiments.
Detailed Description
The method for reducing the color noise of the image is applied to the intelligent electronic equipment, preferably, the intelligent electronic equipment is provided with an image pickup device, such as a camera, and the like, the image pickup device is provided with an image sensor, such as a CMOS, and the intelligent electronic equipment acquires an initial image by using the image pickup device. Preferably, the intelligent electronic device is provided with a processor and a memory, the memory is stored with a computer program, and the processor implements the method for reducing the color noise of the image by executing the computer program.
The embodiment of the method for reducing the color noise of the image comprises the following steps:
the embodiment mainly adopts a sampling storage mode for an initial image acquired by an image sensor, and performs sampling of preset multiples on rows and columns in an initial denoising region corresponding to a pixel to be denoised so as to reduce the data volume and the calculation complexity of the pixel required to be stored in the denoising process. In addition, the color noise of the image sensor is calibrated, the denoising intensity is obtained by combining the nonlinear sensitivity characteristic of human eyes to the brightness, and the image is denoised by using the denoising intensity. In addition, a specific tone color protection mechanism is added in the embodiment, the data of the chrominance value after denoising is corrected, and the reality of the color of the image after denoising is improved.
The present embodiment will be described in detail with reference to fig. 1. First, step S1 is performed, an initial image is acquired, and the initial image is preprocessed. In this embodiment, the initial image is an image output by a CMOS image sensor, and the color information of the initial image may be RGB information or YCbCr information. The present embodiment mainly processes an image having YCbCr information, and therefore, if an initial image output by the CMOS image sensor is an RGB image, the initial image needs to be preprocessed to obtain YCbCr information of each pixel. Where Y is the luminance value of the pixel, Cb is the blue chrominance value of the pixel, and Cr is the red chrominance value of the pixel. The present embodiment deals with color noise of an image based on Cb and Cr of each pixel.
If the image is an RGB image and the color information of each pixel includes R (red), G (green), and B (blue) information, the Cb, Cr information may be calculated using the following formula, i.e., calculating the blue chrominance value and the red chrominance value of each pixel.
Cr ═ G-R; Cb-G (formula 1)
If the initial image output by the CMOS image sensor is a YCbCr image, each pixel Cb and Cr information is directly used.
Then, each pixel of the initial image is subjected to denoising calculation to obtain an output colorimetric value. Specifically, step S2 is executed first, and the currently acquired pixel is taken as a pixel to be denoised, so as to acquire an initial denoising region corresponding to the pixel to be denoised. Specifically, for each pixel to be denoised, pixels in a certain region centered on the pixel constitute an initial denoising region. For example, the present embodiment uses 33 rows and 65 columns of pixels centered on the pixel to be denoised as the initial denoising region.
As shown in FIG. 2, assume that the pixel to be denoised is P16,32Then P is0,0For a pixel P to be denoised16,32The upper 16 rows and the left 32 columns of pixels, which are the pixels in the upper left corner of the initial denoising region. Similarly, then P32,64For a pixel P to be denoised16,32The lower 16 rows, the right 32 columns of pixels, which is the pixel in the lower right corner of the initial de-noising region, and so on.
As can be seen from fig. 2, the initial denoising region has a total of 2145(33 × 65) pixels, and assuming that each pixel needs to use 8 bits of storage space to store the data of the chroma value, 17160 bits of storage space are needed to store the chroma value data of all the pixels of the entire initial denoising region. In addition, since the mean filtering calculation is required when performing the denoising calculation on the pixel to be denoised, 2145 times of mean filtering calculation is required.
In order to reduce the storage space and reduce the calculation amount of the mean filtering, the present embodiment obtains a new denoising region in a sampling manner, i.e., performs step S3. As shown in fig. 3, in the present embodiment, 4 times of the preset times are used as the preset times, the initial denoising region is sampled and stored 4 times in the row direction and the column direction, that is, the pixels in the 32 th row of … of the 0 th row, the 4 th row and the 8 th row, and the pixels in the 64 th column of … of the 0 th column, the 4 th column and the 8 th column are extracted, and the extracted pixels are used as a new denoising region, which is a target denoising region.
Thus, with the pixel P to be denoised16,32In the target denoising area, the number of pixels is 153(9 × 17), and since the chrominance value of each pixel includes a red chrominance value Cr and a blue chrominance value Cb, assuming that the storage space of the chrominance value of each pixel is 8 bits, this embodiment only needs 1224 bits of storage space to store chrominance value data of all pixels in the target denoising area. In addition, the average filtering calculation performed subsequently only needs to be performed 153 times, and the calculation amount is greatly reduced.
In the present embodiment, the sampling storage is performed by 4 times, and in practical application, the sampling storage may be performed by 2 times, 3 times, or 8 times, and the preset multiple may be an integer multiple of 2 times or more. In addition, when sampling storage is performed, uniform sampling storage is required, namely, one row or one column is extracted from the row or the column at preset times, and the row or the column of sampling is not concentrated in a small area of the initial denoising area. In the case of sample storage, only the rows may be sampled, only the columns may be sampled, or both the rows and the columns may be sampled. If the rows and columns are sampled simultaneously, the sampling multiples for the rows and columns may be the same or different, for example, 4 times for the rows and 2 times for the columns.
Then, step S4 is executed to obtain an initial chroma value of the pixel to be denoised, that is, to obtain a pixel P to be denoised16,32A red chrominance value Cr and a blue chrominance value Cb. Next, step S5 is executed to perform mean filtering calculation on the initial chrominance values of the pixels to be denoised. Specifically, the mean filtering calculation is performed according to the following method.
First, a preset distance value between each pixel in the target denoising region and the pixel to be denoised is calculated, where the preset distance value in this embodiment is a simplified euclidean distance, and is calculated using the following formula, for example.
d(i,j)＝|P16,32-Pi,jI (formula 2)
Wherein d (i, j) is the calculated distance value, P16,32And a red chromatic value Cr or a blue chromatic value Cb of the pixel to be denoised, wherein i is the row number of the pixel and is 0, 4 or 8 … 32, j is the column number of the pixel and is 0, 4 or 8 … 64.
Of course, the simplified euclidean distance is adopted in the formula 2, and other distances may be used as the preset distance value in practical applications, such as a manhattan distance, a minkoch distance, a chebyshev distance, a cosine distance, and the like, and the same effect may be obtained.
Then, a variable SUM _ P and a variable CNT are defined, where the variable SUM _ P is an accumulated value of pixel chroma values meeting requirements, the variable CNT is the number of pixels meeting requirements, and the initial values of the variable SUM _ P and the variable CNT are both 0. Then, comparing the calculated distance value d (i, j) with a preset noise threshold value NP, and judging a certain pixel and a pixel P to be denoised16,32If the distance value d (i, j) of (c) is less than the noise threshold NP, it indicates that the pixel is a satisfactory pixel, and the chrominance value of the pixel is added to the variable SUM _ P, and the variable CNT is self-incremented once. After traversing all pixels in the target denoising region, the variables are changedThe value of SUM _ P is the SUM of the chrominance values of all the satisfactory pixels, and the value of variable CNT is the number of all the satisfactory pixels.
In this embodiment, the noise threshold NP is a pre-calculated threshold, and specifically, may be calculated by using the following formula.
Wherein the content of the first and second substances,
is the characteristic value of the image sensor, is obtained according to the characteristic calibration of the CMOS image sensor, and is related to the chromaticity
Is related to the size of (a). Therefore, the noise threshold NP is positively correlated with the characteristic value of the image sensor.
And NP2 (Y)16,32) In this embodiment, the curve of the color noise sensitivity value and the variation curve of the pixel brightness are shown in fig. 4. As can be seen from fig. 4, the color noise sensitivity value is not changed linearly but is changed in a curve according to the change of the pixel brightness, and the slope of a section of the curve in the middle of the brightness value is greater than the slopes of both ends, so that when the brightness value of the pixel is a section of the middle value, the change rate of the color noise sensitivity value is greater than the change rate of the pixel with a lower or higher brightness value. From equation 3, the noise threshold NP of the present embodiment is positively related to the color noise sensitivity value.
After traversing all pixels in the target denoising region, calculating a mean filtering value of the pixels to be denoised, wherein the mean filtering value is calculated by adopting the following formula:
as shown in equation 4, the accumulated value of the chrominance values of the pixels meeting the requirement is divided by the number of the pixels meeting the requirement in the embodimentMean value filtering value P of pixel to be denoised16,32NR, where the mean value of the pixel to be denoised is filtered by a value P16,32The _ NR includes a red chrominance value Cr _ NR and a blue chrominance value Cb _ NR.
As can be seen, in the embodiment, performing mean filtering on the initial chroma values of the pixels to be denoised is to calculate a preset distance value between the chroma value of each pixel in the target denoising region and the initial chroma value of the pixels to be denoised, and calculate an average value of the chroma values of all pixels of which the preset distance values are smaller than the noise threshold.
If the value after the mean filtering is directly output as the output chromatic value of the pixel to be denoised, the situation that the saturation of some colors (such as the green of trees) after denoising is too low or the color distortion is serious is easily caused. Therefore, after step S5 is executed, step S6 is further executed to perform a color protection calculation on the average-filtered chrominance value to obtain an output chrominance value, which may specifically be calculated by using the following formula.
Cr _ final ═ Cr _ NR × (1-W) + Cr × W (formula 5)
Cb _ final ═ Cb _ NR × (1-W) + Cb × W (formula 6)
Wherein W is a predetermined protection weight, and
and (4) correlating.
Then, step S7 is executed to determine whether conversion of color information is required for the chrominance values of the pixels. Since the red colorimetric value Cr _ final and the blue colorimetric value Cb _ final of the pixel are obtained by calculation in the present embodiment, if the input image is an RGB image, the colorimetric values of the pixel need to be converted into RGB information, that is, if the determination result of step S7 is yes, step S8 is performed to perform conversion calculation of color information on the output colorimetric values. Specifically, the following formula may be used to convert the red chrominance value Cr _ final and the blue chrominance value Cb _ final into RGB information, resulting in output data G _ final, R _ final, and B _ final of the three primary colors.
R_final＝G_final-Cr_final
B _ final ═ G _ final-Cb _ final (equation 7)
Of course, if the image output by the CMOS image sensor is a YCbCr image, the red chrominance value Cr _ final and the blue chrominance value Cb _ final are directly output, i.e., step S9 is performed.
Finally, step S10 is executed to determine whether the denoising calculations of all pixels of the initial image are completed, if the denoising calculations of all pixels of the initial image are completed, the data of the image can be output, and if not, step S11 is executed to obtain the next pixel, and the process returns to step S2 to obtain the initial denoising region corresponding to the pixel, and perform denoising calculations on the pixel until all pixels are completed.
Therefore, in the embodiment, the initial denoising region is sampled and stored, and the number of pixels of the target denoising region is greatly reduced compared with that of the initial denoising region, so that the occupied storage space of a memory is reduced, and the calculation amount of the mean value filtering is also greatly reduced.
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 method for reducing the color noise of the image. 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.
A computer-readable storage medium:
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 method for reducing the color noise of the image.
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 is emphasized that the invention is not limited to the above-described embodiments, such as variations of the multiples of the sample storage, or variations of the specific algorithm for performing the mean filtering, etc., which variations are also intended to be included within the scope of the claims.
Claims (10)
1. A method of reducing color noise in an image, comprising:
acquiring an initial image, and calculating an output chromatic value of each pixel of the initial image;
wherein calculating the output chrominance value for each pixel comprises:
acquiring an initial chromatic value of a pixel to be denoised, acquiring an initial denoising region corresponding to the pixel to be denoised, and sampling and storing the pixels in the initial denoising region by a preset multiple to obtain a target denoising region, wherein the preset multiple is an integral multiple greater than 2;
and acquiring a chromatic value of each pixel in the target denoising area, performing mean filtering on the initial chromatic value of the pixel to be denoised, and calculating an output chromatic value of the pixel to be denoised according to the chromatic value after the mean filtering.
2. The method for reducing image color noise according to claim 1, wherein:
calculating the output chroma value of the pixel to be denoised according to the chroma value after mean value filtering comprises the following steps: and carrying out preset color protection calculation on the colorimetric values after the average value filtering to obtain the output colorimetric values.
3. The method for reducing image color noise according to claim 2, wherein:
the color protection meter includes: and calculating the output colorimetric value by using a preset protection weighted value and the average filtered colorimetric value.
4. A method for reducing image color noise according to any one of claims 1 to 3, wherein:
sampling and storing pixels in the initial denoising region by preset times comprises the following steps: and sampling and storing the row number and/or the column number of the pixels in the initial denoising area by the preset multiple.
5. A method for reducing image color noise according to any one of claims 1 to 3, wherein:
performing mean filtering on the initial chrominance values of the pixels to be denoised comprises: and calculating a preset distance value between the chromatic value of each pixel in the target denoising area and the initial chromatic value of the pixel to be denoised, and calculating the average value of the chromatic values of all the pixels of which the preset distance values are smaller than a noise threshold value.
6. The method for reducing image color noise according to claim 5, wherein:
the noise threshold is positively correlated to the color noise sensitivity value, which increases with increasing luminance of the pixel.
7. The method for reducing image color noise according to claim 5, wherein:
the noise threshold is positively correlated to the characteristic value of the image sensor.
8. The method for reducing image color noise according to claim 5, wherein:
the preset distance value is one of a simplified euclidean distance, a manhattan distance, a minkoch distance, a chebyshev distance, and a cosine distance.
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 of reducing image color noise 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 performs the steps of the method of reducing image color noise according to any one of claims 1 to 8.
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