CN111161188B - Method for reducing image color noise, computer device and 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 an output chromaticity value of each pixel of the initial image; wherein calculating the output chromaticity value of each pixel of the initial image comprises: acquiring an initial chroma value of a pixel to be denoised, acquiring an initial denoising region corresponding to the pixel to be denoised, sampling and storing pixels in the initial denoising region by a preset multiple to acquire a target denoising region, wherein the preset multiple is an integer multiple larger than 2; and obtaining the chromaticity value of each pixel in the target denoising region, carrying out average filtering on the initial chromaticity value of the pixel to be denoised, and calculating the output chromaticity value of the pixel to be denoised according to the chromaticity value after average filtering. The invention also provides a computer device for realizing the method and a computer readable storage medium. The invention can reduce the data storage capacity and the operation amount in the image denoising calculation process.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method for reducing image color noise, a computer device for realizing the method and a computer readable storage medium.
Background
Many existing intelligent electronic devices have an image capturing function, for example, a smart phone, a tablet computer, a vehicle recorder and the like are all provided with an image capturing device, and the image capturing device is usually provided with a CMOS sensor to obtain an image. Typically, an image includes a large number of pixels, and color information of each pixel may be represented by an RGB value or a YUV value.
For example, CMOS image sensors currently in common use generally employ a BAYER arrangement format, where each pixel has only one color information, that is, RGB values, but RGB values are not three primary colors, and thus, an image may be distorted, and thus, each pixel needs to be "demosaiced" to obtain RGB three primary colors to restore the original color of the image.
As image resolution increases, the amount of light sensed by individual pixels decreases, and low-light scenes are increasingly used, image noise output by CMOS image sensors increases greatly. 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 RGB three primary colors of one pixel, noise of each color component is diffused mutually in a large range, so that a large block (from a few pixels to hundreds of pixels) of color spots appear in a final image, and the visual perception of human eyes is seriously influenced. Therefore, it is generally necessary to perform denoising processing on an image output from the CMOS image sensor.
The currently commonly used method for reducing color noise is a method of adopting average filtering to the chroma value of each pixel, the current method is to select one pixel as the pixel to be denoised, and select a certain number of rows and columns of pixels with the pixel as the center as a denoised area, for example, 33 rows and 65 columns of pixels are selected as the denoised area, thus, the number of pixels in the denoised area is 2145 (33×65), and since average filtering calculation is required, the data of the chroma value of each pixel needs to be stored, and taking the chroma value of each pixel as 8bit as an example, a 17160bit storage space is required to be able to store the chroma values of all pixels in the denoised area. In addition, 2145 times of mean filtering calculation are needed when the mean filtering is performed, and the calculated amount is very large.
Clearly, the current color noise reduction method has the following problems: because the range of the denoising area is very large, more than tens lines of pixels need to be cached for denoising under normal conditions, a large amount of storage resources and calculation resources are occupied, so that the intelligent electronic equipment can realize the color noise operation of reducing the image only by needing to configure a larger memory and a processor with stronger calculation capability, and the production cost of the intelligent electronic equipment is higher. In addition, when filtering image color noise, the true color details of the object can not be ensured at the same time, and the color distortion of the image is easy to cause.
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 intelligent electronic equipment.
Another object of the present invention is to provide a computer apparatus implementing the above method for reducing image color noise.
It is still another object of the present invention to provide a computer readable storage medium embodying the above method of reducing image color noise.
In order to achieve the main object of the present invention, the present invention provides a method for reducing color noise of an image, comprising obtaining an initial image, and calculating an output chromaticity value of each pixel of the initial image; wherein calculating the output chromaticity value of each pixel of the initial image comprises: acquiring an initial chroma value of a pixel to be denoised, acquiring an initial denoising region corresponding to the pixel to be denoised, sampling and storing pixels in the initial denoising region by a preset multiple to acquire a target denoising region, wherein the preset multiple is an integer multiple larger than 2; and obtaining the chromaticity value of each pixel in the target denoising region, carrying out average filtering on the initial chromaticity value of the pixel to be denoised, and calculating the output chromaticity value of the pixel to be denoised according to the chromaticity value after average filtering.
According to the scheme, after the initial denoising area is obtained, sampling and storing are carried out on the initial denoising area to form the target denoising area, and the number of pixels of the target denoising area is greatly reduced, so that the data size of the pixel chromaticity value required to be stored is greatly reduced, the calculated size is also greatly reduced when average filtering is carried out, and the requirement on hardware of intelligent electronic equipment is reduced. In addition, since the initial denoising region is stored in a sampling mode to obtain the target denoising region, and the difference between the chromaticity values of adjacent pixels is not large, the color authenticity of the denoised image is not obviously reduced by the method, and particularly for high-resolution images, the color of the image is not obviously distorted.
In a preferred embodiment, calculating the output chroma value of the pixel to be denoised from the average filtered chroma value includes: and carrying out preset color protection calculation on the chromaticity value subjected to the mean value filtering to obtain an output chromaticity value.
Therefore, the color protection calculation is performed on the color value after the mean value filtering, so that certain colors after denoising, such as the situations of too low saturation of the green of the tree or serious color distortion, can be avoided, and the color authenticity of the image after denoising is improved.
Further, the color protection meter comprises: and calculating an output chromaticity value by using a preset protection weight value and the chromaticity value after mean value filtering.
Therefore, a protection weight value is preset to calculate the output chromaticity value, color protection calculation can be rapidly realized, the denoising efficiency of the image is improved, and the problem that the image can be displayed only for a long time due to overlong denoising calculation time of the image is avoided.
In a further aspect, sampling and storing pixels in the initial denoising region by a preset multiple includes: and sampling and storing the number of rows and/or columns of pixels in the initial denoising region according to a preset multiple.
Therefore, pixels in the initial denoising region are sampled in a row and column mode, pixel sampling storage can be rapidly realized, and the time required by image sampling storage is reduced.
In a preferred embodiment, the average filtering of the initial chrominance value of the pixel to be denoised comprises: and calculating a preset distance value between the chromaticity value of each pixel in the target denoising region and the initial chromaticity value of the pixel to be denoised, and calculating the average value of the chromaticity values of all pixels with preset distance values smaller than the noise threshold.
Therefore, the situation of the chromaticity value of the pixel to be denoised can be truly reflected by calculating the sum of the chromaticity values of the pixels with the preset distance value smaller than the noise threshold value and dividing the sum by the average value obtained by the number of the pixels with the preset distance value smaller than the noise threshold value, and the situation of the chromaticity value mutation of the pixel to be denoised can be effectively avoided.
Still further, the noise threshold is directly related to the color noise sensitivity value, which increases as the brightness of the pixel increases.
It can be seen that the color noise sensitivity value is preset, and the noise threshold value is calculated by the color noise sensitivity value, so that the calculated noise threshold value is related to the brightness of the pixel. Since color noise has a certain relation with the brightness of a pixel, for example, the reason why color noise is generated may be that insufficient illumination is caused, by introducing a color noise sensitivity value related to brightness to calculate a noise threshold value, the color authenticity of a denoised image can be improved.
Still further, the noise threshold is directly related to the characteristic value of the image sensor. In this way, the set noise threshold can reflect the characteristics of the image sensor, and the image can be subjected to targeted denoising according to the characteristics of the image sensor.
Further, the preset distance value is one of a simplified euclidean distance, a manhattan distance, a Min Shi distance, a chebyshev distance and a cosine distance.
It can be seen that the simplified euclidean distance, manhattan distance, min Shi distance, chebyshev distance, and cosine distance are all common distance values used for image filtering calculation, and the workload of image denoising calculation can be simplified by using the distance values.
To achieve the above another object, the present invention provides a computer apparatus including a processor and a memory, the memory storing a computer program, the computer program implementing each step of the above method for reducing image color noise when executed by the processor.
To achieve still another object of the present invention, there is provided a computer program stored on a computer readable storage medium, which when executed by a processor, implements the steps of the above method for reducing color noise of an image.
Drawings
FIG. 1 is a flow chart of an embodiment of a method of reducing image color noise according to the present invention.
FIG. 2 is a schematic diagram of pixel storage of an initial denoising region in an embodiment of a method of reducing image color noise according to the present invention.
FIG. 3 is a schematic diagram of pixel storage of a target denoising region in an embodiment of a method of reducing image color noise according to the present invention.
FIG. 4 is a graph showing the variation of the color noise sensitivity value with the brightness of a pixel in an embodiment of a method for reducing the color noise of an image according to the present invention.
The invention is further described below with reference to the drawings and examples.
Detailed Description
The method for reducing the color noise of the image is applied to 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 (complementary metal oxide semiconductor), 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 having stored thereon a computer program, the processor implementing a method of reducing image color noise by executing the computer program.
Method embodiments for reducing image color noise:
the method mainly adopts a sampling storage mode aiming at an initial image acquired by an image sensor, and samples the rows and columns in an initial denoising area corresponding to the pixels to be denoised by a preset multiple so as to reduce the data quantity and the calculation complexity of the pixels required to be stored in the denoising process. In addition, through calibrating the color noise of the image sensor image and combining the nonlinear sensitivity characteristic of human eyes to the brightness, the denoising intensity is obtained, and the denoising intensity is used for denoising the image. In addition, the embodiment also adds a specific tone color protection mechanism to correct the denoised chromaticity value data and improve the color authenticity of the denoised image.
The present embodiment will be described in detail with reference to fig. 1. First, step S1 is performed to acquire an initial image, and the initial image is preprocessed. In this embodiment, the initial image is an image output by the CMOS image sensor, and the color information of the initial image may be RGB information or YCbCr information. The present embodiment mainly deals with an image having YCbCr information, and therefore, if an initial image output by a 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 processes color noise of an image based on Cb and Cr of each pixel.
If the image is an RGB image, the color information of each pixel includes R (red), G (green), and B (blue) information, cb, cr information may be calculated using the following formulas, i.e., calculating a blue chromaticity value and a red chromaticity value of each pixel.
cr=g-R; cb=g-B (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, a denoising calculation is performed for each pixel of the initial image, and an output chromaticity value is obtained. Specifically, step S2 is executed first, where the currently acquired pixel is taken as a pixel to be denoised, and an initial denoising area corresponding to the pixel to be denoised is acquired. Specifically, for each pixel to be denoised, pixels in a certain area centered on the pixel constitute an initial denoising area. For example, the present embodiment uses 33 rows and 65 columns 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 P 16,32 P is then 0,0 For the pixel P to be denoised 16,32 The upper 16 rows and left 32 columns of pixels are the pixels in the upper left corner of the initial denoising region. Similarly, then P 32,64 For the pixel P to be denoised 16,32 The lower 16 rows, right 32 columns of pixels, which are the pixels in the lower right hand corner of the initial denoising 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-bit storage space to store the chromaticity value data, 17160-bit storage space is required to store the chromaticity value data of all pixels of the entire initial denoising region. In addition, since the average value filtering calculation is required when the denoising calculation is performed on the pixel to be denoised, the calculation of 2145 times of average value filtering is required.
In order to reduce the storage space and reduce the calculation amount of the mean filtering, the present embodiment acquires a new denoising region in a sampling manner, that is, performs step S3. As shown in fig. 3, in this embodiment, 4 times of the preset multiple is used to sample and store the initial denoising region in the row direction and the column direction, that is, the pixels in the 0 th row, the 4 th row, and the 32 nd row of the 8 th row … are extracted, the pixels in the 0 th column, the 4 th column, and the 8 th column … are extracted, and the extracted pixels are used as new denoising regions, and the denoising regions are target denoising regions.
Thus, the pixel P is to be denoised 16,32 In the target denoising area with the center, the number of pixels is 153 (9×17), and since the chrominance value of each pixel includes the red chrominance value Cr and the blue chrominance value Cb, the storage space of the chrominance value of each pixel is assumed to be 8 bits, and in this embodiment, only 1224 bits of storage space is required to store the chrominance value data of all pixels in the target denoising area. In addition, the average filtering calculation to be performed later is only needed to be performed 153 times, so that the calculation amount is greatly reduced.
In this embodiment, sampling and storage are performed by 4 times, and in practical application, sampling and storage may be performed by 2 times, 3 times, or 8 times, etc., and the preset times may be an integer multiple of 2 times or more. In addition, when sampling storage is performed, the sampling storage should be uniform, that is, one row or one column is extracted from the rows or columns of preset multiples every interval, instead of focusing the sampled rows or columns in a small area of the initial denoising area. In addition, in the case of sampling storage, only the row may be sampled, only the column may be sampled, or both the row and the column may be sampled. If the rows and columns are sampled simultaneously, the sampling times for the rows and columns may be the same or may be different, e.g., 4 times for the rows and 2 times for the columns.
Then, step S4 is performed to obtain the initial chromaticity value of the pixel to be denoised, namely, the pixel P to be denoised 16,32 Red chrominance value Cr and blue chrominance value Cb of (c). Next, step S5 is executed to perform mean filtering calculation on the initial chroma value of the pixel to be denoised. Specifically, the average 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, and the preset distance value in this embodiment is a simplified euclidean distance, for example, calculated using the following formula.
d(i,j)＝|P 16,32 -P i,j | (2)
Wherein d (i, j) is calculated as the distance value, P 16,32 The red chromaticity value Cr or the blue chromaticity value Cb, i representing the pixel to be denoised is the number of rows of the pixel, the values 0, 4, 8 … 32, j is the number of columns of the pixel, and the values 0, 4, 8 ….
Of course, the simplified euclidean distance is adopted in the formula 2, and other distances can be used as preset distance values in practical application, for example, manhattan distance, min Shi distance, chebyshev distance, cosine distance and the like, and the same effect can be obtained.
Then, a variable sum_p and a variable CNT are defined, the variable sum_p is an accumulated value of chromaticity values of pixels meeting requirements, the CNT is the number of pixels meeting requirements, and initial values of the variable sum_p and the variable CNT are all 0. Then, comparing the calculated distance value d (i, j) with a preset noise threshold NP to judge a pixel and a pixel P to be denoised 16,32 If the distance value d (i, j) of the pixel is smaller than the noise threshold value NP, the pixel is indicated to be a satisfactory pixel, the chromaticity value of the pixel is accumulated to the variable sum_p, and the variable CNT is self-increased once. After traversing all pixels in the target denoising region, the value of variable SUM_P is the SUM of the chromaticity 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, can be calculated using the following formula.
Wherein,,is the characteristic value of the image sensor, is obtained according to the characteristic calibration of the CMOS image sensor, and is equal to the chromaticity +.>Is related to the size of (a). Thus, the noise threshold NP is positiveAnd the characteristic value of the image sensor.
While NP2 (Y) 16,32 ) The color noise sensitivity value increases with the brightness of the pixel, and in this embodiment, the curve of the color noise sensitivity value and the brightness of the pixel is shown in fig. 4. As can be seen from fig. 4, the color noise sensitivity value varies with the brightness of the pixel, not linearly, but in a curve, and the slope of a curve in the middle of the brightness value is larger than that of the two ends, so that the rate of change of the color noise sensitivity value is larger when the brightness value of the pixel is a middle value than when the brightness value is lower or higher. As can be seen 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 the mean value filtering value of the pixels to be denoised, wherein the mean value filtering value is calculated by adopting the following formula:
as can be seen from fig. 4, in this embodiment, the average value P of the pixels to be denoised is obtained by dividing the accumulated value of the chrominance values of the pixels meeting the requirements by the number of the pixels meeting the requirements 16,32 NR, wherein the average filtering value P of the pixel to be denoised 16,32 The_nr includes a red chrominance value cr_nr and a blue chrominance value cb_nr.
It can be seen that, in this embodiment, the average value filtering is performed on the initial chroma values of the pixels to be denoised, which is to calculate the preset distance value between the chroma value of each pixel in the target denoising region and the initial chroma value of the pixel to be denoised, and calculate the average value of the chroma values of all pixels whose preset distance values are smaller than the noise threshold.
If the value after mean filtering is directly output as the output chromaticity value of the pixel to be denoised, the situation that saturation of certain colors (such as green of trees) is too low or color distortion is serious after denoising is easily caused, and in order to prevent the situation, a color protection mechanism is introduced in the embodiment, and the final output chromaticity value is corrected. Therefore, after step S5 is performed, step S6 is further performed to perform color protection calculation on the average filtered chromaticity value to obtain an output chromaticity value, which may be specifically calculated 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 preset protection weight, andand (5) correlation.
Then, step S7 is performed to determine whether conversion of color information is required for the chromaticity value of the pixel. Since in the present embodiment, the red chrominance value cr_final and the blue chrominance value cb_final of the pixel are obtained by calculation, if the input image is an RGB image, the chrominance values of the pixel need to be converted into RGB information, that is, if the determination result in step S7 is yes, step S8 is executed to perform conversion calculation of the color information on the output chrominance values. Specifically, the red chromaticity value cr_final and the blue chromaticity value cb_final may be converted into RGB information using the following formulas, 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 (formula 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 calculation of all pixels of the initial image is completed, if yes, the data of the image can be output, if no, step S11 is executed to obtain the next pixel, step S2 is returned, the initial denoising area corresponding to the pixel is obtained, and denoising calculation is performed on the pixel until all pixels are completed.
Therefore, in this embodiment, by sampling and storing the initial denoising region, the number of pixels in the target denoising region is greatly reduced compared with that in the initial denoising region, so that the storage space of the occupied memory is reduced, and the calculation amount of average filtering is also greatly reduced.
Computer apparatus embodiment:
the computer apparatus of this embodiment may be an intelligent electronic device, and the computer apparatus includes a processor, a memory, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for reducing image color noise. Of course, the intelligent electronic device further comprises an image capturing device for acquiring the initial image.
For example, a computer program may be split into one or more modules, which are stored in memory and executed by a processor to perform the various modules of the invention. One or more of the modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
The processor referred to in the present invention may be a central processing unit (Central Processing Unit, CPU), or other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, 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 a control center of the terminal device, and the various interfaces and lines being used to connect the various parts of the overall terminal device.
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 (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Computer-readable storage medium:
the computer program stored in the above-mentioned computer means may be stored in a computer readable storage medium if it is implemented in the form of software functional units and sold or used as a separate product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the method for reducing color noise of an image when the computer program is executed by a processor.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
Finally, it should be emphasized that the invention is not limited to the above-described embodiments, for example variations in the multiple of the sample storage, or variations in the specific algorithm for performing the mean filtering, etc., which are intended to be included within the scope of the claims.
Claims (8)
1. A method of reducing image color noise, comprising:
acquiring an initial image, and calculating an output chromaticity value of each pixel of the initial image;
wherein calculating the output chromaticity value for each pixel comprises:
acquiring an initial chroma value of a pixel to be denoised, acquiring an initial denoising region corresponding to the pixel to be denoised, sampling and storing pixels in the initial denoising region by a preset multiple to obtain a target denoising region, wherein the preset multiple is an integer multiple larger than 2;
obtaining the chromaticity value of each pixel in the target denoising region, carrying out average filtering on the initial chromaticity value of the pixel to be denoised, and calculating the output chromaticity value of the pixel to be denoised according to the chromaticity value after average filtering;
the average filtering of the initial chroma value of the pixel to be denoised comprises the following steps: calculating a preset distance value between the chromaticity value of each pixel in the target denoising region and the initial chromaticity value of the pixel to be denoised, and calculating the average value of the chromaticity values of all pixels with the preset distance value smaller than a noise threshold value;
calculating the output chroma value of the pixel to be denoised by using the chroma value after mean value filtering comprises: and carrying out preset color protection calculation on the chromaticity value subjected to the mean value filtering to obtain the output chromaticity value.
2. The method of reducing image color noise according to claim 1, wherein:
the color protection meter includes: and calculating the output chromaticity value by using a preset protection weight value and the chromaticity value after mean value filtering.
3. A method of reducing image colour noise according to claim 1 or 2, characterised in that:
sampling and storing pixels in the initial denoising region at a preset multiple comprises: and sampling and storing the number of rows and/or columns of pixels in the initial denoising region according to the preset multiple.
4. The method of reducing image color noise according to claim 1, wherein:
the noise threshold is directly related to the color noise sensitivity value, which increases as the brightness of the pixel increases.
5. The method for reducing image color noise according to claim 4, wherein:
the noise threshold is directly related to the characteristic value of the image sensor.
6. The method of reducing image color noise according to claim 1, wherein:
the preset distance value is one of a simplified Euclidean distance, a Manhattan distance, a Min Shi distance, a Chebyshev distance and a cosine distance.
7. Computer device, characterized in that it comprises a processor and a memory, said memory storing a computer program which, when executed by the processor, implements the steps of the method of reducing image color noise according to any one of claims 1 to 6.
8. A computer readable storage medium having stored thereon a computer program characterized by: the computer program, when executed by a processor, carries out the steps of the method of reducing image color noise according to any one of claims 1 to 6.
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| CN111681187B (en) * | 2020-06-13 | 2023-06-16 | 厦门美图之家科技有限公司 | Method and device for reducing color noise, electronic equipment and readable storage medium |
| CN111784603B (en) * | 2020-06-29 | 2024-01-26 | 珠海全志科技股份有限公司 | RAW domain image denoising method, computer device and computer readable storage medium |
| CN111784605B (en) * | 2020-06-30 | 2024-01-26 | 珠海全志科技股份有限公司 | Image noise reduction method based on region guidance, computer device and computer readable storage medium |
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