Color noise reduction method, apparatus, electronic device, and computer-readable medium

Abstract

This application provides a color noise reduction method, apparatus, electronic device, and computer-readable medium. The color noise reduction method includes the steps of: converting a source image in YUV format into a target image in a target format, which is a color encoding format that does not use lumens for encoding; calculating pixel difference values ​​between adjacent pixel points in the target image and obtaining a difference value distribution; and performing noise reduction on the source image based on the difference value distribution. By indirectly introducing Y channel information in the source image using an image in the target format, the similarity between adjacent region pixels can be effectively distinguished, color noise reduction can be maximized in the edge region, and the problems of color bleeding and residual edge noise in color noise reduction can be solved.

Description

Technical Field

[0001] This application belongs to the field of image processing, and specifically relates to a color noise reduction method, apparatus, electronic device, and computer-readable medium.

Background Art

[0002] In application scenarios of image sensors such as mobile phones and video surveillance, for example, CMOS Sensor (Complementary Metal-Oxide-Semiconductor Sensor), CCD Sensor (Charge Coupled Device Sensor), etc., it is common for an image to contain some low-frequency noise, which exists as bright patches of low-frequency colors in the RGB image. In the YUV format, these low-frequency noises are more prominent in the UV channel. Therefore, a method of removing low-frequency color noise in an image by reducing noise in the UV channel is often used.

[0003] However, the noise reduction method according to the prior art has difficulty in achieving a good balance between color noise and color bleeding at the image edge, and the effect is not very good.

Summary of the Invention

Problems to be Solved by the Invention

[0004] In view of this, an object of this application is to provide a color noise reduction method, apparatus, electronic device, and computer-readable medium that can solve the problem of difficulty in suppressing color noise at the image edge by the conventional noise reduction method.

Means for Solving the Problems

[0005] In the first part, the present application provides a color noise reduction method. The color noise reduction method includes the steps of: converting a source image in YUV format into a target image in a target format which is a color encoding format that does not use rumas for encoding; calculating pixel difference values ​​between adjacent pixel points in the target image and obtaining a difference value distribution; and performing noise reduction on the source image based on the difference value distribution.

[0006] In the above embodiment, by indirectly introducing Y channel information from the source image using an image of the target format, the similarity between adjacent region pixels can be effectively distinguished, color noise can be reduced to the maximum extent in the edge region, and the problems of color bleeding and residual edge noise in color noise reduction can be solved.

[0007] In one selectable embodiment of the present application, the step of performing noise reduction on the source image based on the difference value distribution includes the steps of calculating a weight parameter for each pixel point in the source image based on the corresponding pixel difference value in the difference value distribution for each pixel point in the source image, and achieving noise reduction by weighting adjacent pixels corresponding to each pixel point in the source image based on the weight parameter of each pixel point.

[0008] In the above embodiment, the target image is weighted as a guide image for the source image, and the Y channel information of the source image is indirectly introduced by the target image. This makes it possible to effectively distinguish the similarity between adjacent region pixels and to reduce color noise to the maximum extent possible in the edge region, without color bleeding or residual edge noise.

[0009] In one selectable embodiment of the present invention, the step of calculating pixel difference values ​​between adjacent pixel points in the target image and obtaining a difference value distribution includes the steps of calculating pixel difference values ​​between a pixel point in the target image and all pixel points in the adjacent region of that pixel point and obtaining a difference value matrix, and obtaining a difference value matrix of all pixel points in the target image and obtaining the difference value distribution.

[0010] In the above embodiment, by obtaining the difference value between a pixel point and all pixel points in the adjacent region, it is possible to identify changes in the pixel point in all directions and obtain more information about the pixel point.

[0011] In one selectable embodiment of this application, the adjacent region is a region in the target image within a predetermined radius centered on the pixel point.

[0012] In one selectable embodiment of the present invention, the step of performing noise reduction on the source image based on the difference value distribution includes the steps of obtaining a weight matrix for each pixel point in the source image, and achieving noise reduction by weighting all pixel points in the adjacent region corresponding to each pixel point in the source image based on the weight matrix for each pixel point, wherein the weight matrix is ​​calculated from the difference value matrix of the corresponding pixel points in the target image.

[0013] In the above embodiment, by constructing a weight matrix using the difference value distribution, noise reduction can be performed on the pixel points by making full use of the surrounding region of the pixel points, thereby improving the effect of noise reduction.

[0014] In one selectable embodiment of this application, the target format includes the RGB format.

[0015] In one selectable embodiment of the present application, the step of performing noise reduction on the source image based on the difference value distribution includes performing noise reduction on the U channel and the V channel, respectively, based on the difference value distribution.

[0016] In the second aspect, the embodiment of the present application provides a color noise reduction device. The color noise reduction device comprises a conversion module configured to convert a source image in YUV format into a target image in a target format which is a color encoding format that does not use rumans for encoding; a calculation module configured to calculate pixel difference values ​​between adjacent pixel points in the target image and obtain a difference value distribution; and a noise reduction module configured to perform noise reduction on the source image based on the difference value distribution.

[0017] In the third aspect, embodiments of the present application provide an electronic device. The electronic device comprises a memory and a processor, the processor and the memory being connected, the memory being configured to store a program, and the processor being configured to read the program stored in the memory and execute any one of the methods of the first aspect.

[0018] In the fourth phase, an embodiment of the present application provides a computer-readable medium. A computer program is stored in the computer-readable medium, and when the computer program is executed by a processor, one of the methods of the first phase is executed.

[0019] Other features and advantages of this application will be described in the following sections. The purpose and other advantages of this application can be realized and obtained by the structure specifically shown in the specification and drawings. [Brief explanation of the drawing]

[0020] [Figure 1] This is a flowchart of the color noise reduction method according to the embodiment of this application. [Figure 2] It is a schematic block diagram of a color noise reduction device according to an embodiment of the present application. [Figure 3] It is a schematic block diagram of an electronic device according to an embodiment of the present application.

Embodiments for Carrying Out the Invention

[0021] Hereinafter, embodiments of the present application will be described with reference to the drawings. The same elements in different drawings are denoted by the same reference numerals. In the following description, the specific arrangements and detailed descriptions of the components are only for a complete understanding of the embodiments of the present application. Therefore, as will be understood by those skilled in the art, various changes and modifications can be made to the embodiments described herein without departing from the scope of the present application. Also, for clarity and simplicity, descriptions of well-known functions and structures are omitted. The terms described below are defined based on the functions in the present application and may vary depending on the user, the user's intention or habit, etc. Therefore, the definitions of the terms are determined based on the content of this specification.

[0022] The present application may have various modifications and various embodiments. Hereinafter, embodiments will be specifically described with reference to the drawings. Note that the present application is not limited to the embodiments, and includes all changes, equivalents and alternatives within the scope of the present application.

[0023] The terms used in the present application are only for explaining various embodiments of the present application and do not limit the present application. Unless otherwise specified, the singular form can also refer to the plural. In the present application, the terms "comprising" or "having" indicate that there are features, numbers, steps, operations, components, members or combinations thereof, but do not mean to exclude the existence or addition of one or more other features, numbers, steps, operations, components, members or combinations thereof.

[0024] Unless otherwise specified, the terms used herein have the same meaning as would be understood by one of ordinary skill in the art. In this application, unless otherwise specified, terms (e.g., terms defined in a general dictionary) should be understood to have the same meaning as in the relevant art, and should not be construed only in terms of ideal or formal meanings.

[0025] An electronic device according to an embodiment can be one of each type of electronic device. The electronic device can be, for example, a mobile communication device (e.g., a smartphone), a computer, a portable multimedia device, a portable medical device, a camera, a wearable device, or a household electrical appliance. In one disclosed embodiment, the electronic device is not limited to the above examples.

[0026] The terms used in this application are not intended to limit this application, but include various modifications, equivalents, or alternatives of the corresponding embodiments. For the description of the drawings, like reference numerals represent like or related elements. Unless otherwise specified, the singular form of a noun can represent one or more. Phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" used herein can each include all combinations of the listed items. The terms "first", "second", "primary", and "secondary" used herein are only for distinguishing components and do not limit the components in terms of other aspects (e.g., importance or order). When an element (e.g., a first element) is described as being "coupled" or "connected" to another element (e.g., a second element), whether explicitly or implicitly described by the terms "operatively" or "communicatively", it means that the element is coupled to the other element directly (e.g., wired), wirelessly, or via a third element.

[0027] As used herein, the term "module" may include units implemented by hardware, software, or firmware, and may be used interchangeably with other terms (e.g., "logic," "logic block," "component," and "circuit"). A module may be a single integrated component or its smallest unit or component that performs one or more functions. For example, in one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0028] For ease of understanding, the relevant terms and concepts used in the embodiments of this application are first explained below.

[0029] The term YUV refers to a color encoding method commonly used in various video processing components. YUV takes human perception into consideration when encoding photos or videos, while also allowing for a reduction in chroma bandwidth. YUV represents a set of color spaces, including Y'UV, YUV, Y'CbCr, and YPbPr, each encoding luminance and color information respectively. YUV represents any color with three values, called Y, U, and V. Y is luminance or luma, U is chrominance, and V is chroma.

[0030] For example, in a broad sense, the term YUV also includes the specific color space Y'CbCr used for computer video. The Y' component is called the lumen and represents the lumen value of a color. The sign (') distinguishes lumen from luminance, which is the lumen of a value closely related to lumen and is usually represented by Y. Luminance is derived from linear RGB values, while lumen is derived from non-linear (gamma-corrected) RGB values. Luminance is a closer measure of actual brightness, but for technical reasons, lumen is more practical. The superscript sign is often omitted, but the YUV color space always uses lumen, not luminance. Cb and Cr are the blue difference and red difference, respectively.

[0031] Conventional techniques primarily involve two methods for removing color noise from YUV images.

[0032] 1. Each UV channel calculates a corresponding weight based on the similarity between the current pixel point and neighboring pixel points, and uses these calculated weights to weight neighboring pixels to obtain the result of noise reduction for the current pixel point.

[0033] However, because the UV channel itself only contains low-frequency information from the image, resulting in a very low contrast ratio and making it impossible to distinguish between adjacent pixel points based on similarity, the output results from such color noise reduction algorithms often exhibit significant color bleeding.

[0034] 2. When using only the UV channel, color bleeding is likely to occur. Therefore, Y channel information is introduced to prevent the strong edges of the image from being stained by adjacent pixels. For example, the Y channel is used to detect image edge information, and then color bleeding at the edges is prevented by methods such as reducing the similarity search window for the detected image edges, lowering the noise reduction intensity, or adding some of the original image signal after noise reduction.

[0035] However, while this color noise reduction method can reduce color bleeding to some extent, it often leaves a large amount of noise remaining at the edges of the image.

[0036] In view of this, this application provides a color noise reduction method that can effectively distinguish the similarity between adjacent region pixels and minimize color noise in the edge region by indirectly introducing Y channel information in the source image using an image of the target format, and that does not result in color bleeding or residual edge noise during color noise reduction.

[0037] Figure 1 is a flowchart of a color noise reduction method according to an embodiment of this application. As shown in Figure 1, the method includes steps S110 to S130.

[0038] S110: Converts a source image in YUV format to a target image in a target format that is a color encoding format that does not use ruma for encoding.

[0039] The source image is obtained, and the source image is color-encoded using the YUV format. Color encoding refers to various methods for displaying, storing, and transmitting colors in computer systems and digital devices.

[0040] The source image is converted from the source format to the target format to generate the target image. The target image is color encoded using the target format. The target format is a format that does not use rumen features for color encoding, and examples include RGB, CMYK, HSV / HSL, Hexadecimal Color Codes, and LAB Color Space. This application does not limit the specific form of the target format. The target format does not use rumen features for color encoding. For example, RGB (Red, Green, Blue) creates various colors by mixing red, green, and blue light in different proportions based on the additive color mixing principle of light. Alternatively, HSV / HSL (Hue, Saturation, Value / Lightness) represents color using hue (type of color), saturation (purity of color), and luminance / lightness (degree of brightness of color). These color encoding methods are closer to human perception of color.

[0041] Exemplary, this application uses the RGB format as the target format. A target image, i.e., an RGB image, corresponding to the source image is obtained by converting YUV to RGB. YUV is converted to RGB using the following equations 1 to 3.

[0042] R=Y+1.4075*(V-128) Formula 1 G=Y-0.3455*(U-128)-0.7169*(V-128) Formula 2 B = Y + 1.779 * (U - 128) Equation 3

[0043] S120: Calculate the pixel difference values ​​between adjacent pixel points in the target image and obtain the difference value distribution.

[0044] For each pixel point in the target image, the difference in pixel value between it and its adjacent pixel points is calculated to obtain the pixel difference value for each pixel point in the target image. The difference value distribution of the target image is constructed using the pixel difference values ​​of all pixel points in the target image.

[0045] For example, assuming the image is 8x8, the difference in pixel value between the pixel point at position (4,4) and the pixel point at position (4,3) is calculated, or assuming the image is 8x8, the difference in pixel value between the pixel point at position (4,4) and the pixel points at positions (3,4) and (5,4) is calculated. As a method for calculating the pixel value and its difference, the three RGB channels are weighted, the weighted pixel value is obtained, and the difference in weighted pixel values ​​between the two pixel points is calculated. The method for calculating the pixel value and its difference is not limited to the above example and may be other methods, and is not limited in this application.

[0046] In one embodiment of the present application, the step of calculating pixel difference values ​​between adjacent pixel points in the target image and obtaining a difference value distribution includes the step of calculating pixel difference values ​​between a pixel point in the target image and all pixel points in the adjacent region of that pixel point and obtaining a difference value matrix, and the step of obtaining a difference value matrix of all pixel points in the target image and obtaining the difference value distribution.

[0047] For each pixel point in the target image, the difference in pixel value between that pixel point and all pixel points in its adjacent region is calculated to obtain a difference value matrix for each pixel point. The adjacent region of a pixel point includes at least one of the following: the region adjacent to the pixel point above the pixel point, the region adjacent to the pixel point below the pixel point, the region adjacent to the pixel point to the left of the pixel point, and the region adjacent to the pixel point to the right of the pixel point. The region adjacent to the pixel point above the pixel point is a region composed of one or more pixel points, for example, for a pixel point at position (4,4), the region adjacent above it may be a region composed of the pixel point at position (4,3), or a region composed of the pixel points at positions (3,3), (4,3), and (5,3), or a region diagonally above and adjacent to the pixel point composed of the pixel points at positions (3,3) and (5,3). In some embodiments of this application, the adjacent region is one region or a combination of multiple regions, for example, a combination of the region adjacent to the pixel point to the left of the pixel point and the region adjacent to the pixel point to the right of the pixel point. In the embodiments of this application, the size of the adjacent region, the manner in which it is adjacent to a pixel point, its shape, etc., are not limited.

[0048] For example, if we calculate the difference in pixel value between each pixel point and all the pixel points in the 2x2 area adjacent to its left, and assume the image is 8x8, then for the pixel point at position (4,4), we calculate the difference for all the pixel points in the 2x2 area adjacent to its left. That is, for the pixel point at position (4,4), we calculate the difference in pixel value between it and the pixel points at positions (3,4), (2,4), (3,5), and (2,5). Alternatively, if we calculate the difference in pixel value between each pixel point and all the pixel points in the 1x3 area adjacent to its right, and assume the image is 8x8, then for the pixel point at position (4,4), we calculate the difference in pixel value between it and the pixel points at positions (5,3), (5,4), and (5,5).

[0049] Multiple difference values ​​can be obtained for each pixel point, and by storing these multiple difference values ​​in a predetermined order, a difference value matrix for each pixel point can be generated. For example, for a pixel point at position (4,4), the difference in pixel value between it and all pixel points in the 2x2 region adjacent to its left is calculated, and the absolute value of each difference value is found to obtain a matrix (1,2,3,4). In the above matrix, the difference values ​​for which absolute values ​​have been found are arranged according to the order of the pixel points corresponding to the difference values; that is, difference value 1 corresponds to the absolute value of the difference between the pixel point at position (3,4) and the pixel point at position (4,4).

[0050] Obtain the difference matrix of all pixel points in the target image and construct the difference distribution of the target image. For example, if the difference distribution is D (1,1) , D (1,2) , D (1,3) , ..., D (8,8) D (4,4) For this, the answer is (1,2,3,4).

[0051] In the above embodiment, by obtaining the difference value between a pixel point and all pixel points in the adjacent region, it is possible to identify changes in the pixel point in all directions and obtain more information about the pixel point.

[0052] Furthermore, the adjacent region is a region within a predetermined radius centered on the pixel point in the target image.

[0053] For each pixel point, the adjacent region of that pixel point is a rectangular area within a predetermined radius centered on that pixel point. For example, the adjacent region is an adjacent 3x3 area centered on that pixel point. Assuming the image is 8x8, for the pixel point at position (4,4), the difference value between it and all the pixel points in the adjacent 3x3 region centered on it is calculated. That is, for the pixel point at position (4,4), the difference in pixel value between it and the pixel points at positions (3,3), (3,4), (3,5), (4,3), (4,5), (5,3), (5,4), and (5,5) is calculated. Exemplarily, as part of the pixel value calculation, the difference value between the pixel points in the R channel, G channel, and B channel is calculated for each pixel point, and the difference value between it and all the pixel points in the adjacent region is calculated using Equation 4.

[0054] Diff(x,y,k1,k2)=abs{R(x,y)-R(x+k1,y+k2)}+abs{G(x,y)-G(x+k1,y+k2)+abs{B(x,y)-B(x+k1,y+k2)}} Equation 4

[0055] Diff represents the difference operation, abs represents finding the absolute value, k1 represents the offset value of the row coordinate of the pixel point, with a range of [-r~r], k2 represents the offset value of the column coordinate of the pixel point, with a range of [-r~r], r represents the radius of the value, and x+k1 and y+k2 represent taking a point within an adjacent region with radius r centered on the current point (x,y).

[0056] S130: Noise reduction is performed on the source image based on the difference value distribution.

[0057] After obtaining a difference value distribution from the target image, noise reduction processing is performed on the source image using this difference value distribution. Exemplarily, noise is usually represented as random but high-frequency difference values, while important image content (e.g., edges and textures) is represented as more consistent and regular difference values. By analyzing these difference values, noise and important image content are distinguished. For example, if a single pixel point is set and its corresponding pixel difference value exceeds a predetermined threshold, the pixel point is determined to be a noise pixel point. Alternatively, for example, if the step length is set to 2 and the region is 2x2, and regions of a specific size are sequentially selected according to the step length, and for all pixels in each region there are two or more pixel points whose pixel difference values ​​exceed a threshold, the region is determined to be a noise region. There are various methods for determining noise based on difference values, and are not limited to the above examples. This application is not limited to methods for determining noise. Pixels identified as noise are subjected to a smoothing process, which can be achieved by various filtering techniques such as median filtering, Gaussian filtering, or bilateral filtering. Filters can reduce noise by decreasing the difference in high-frequency frequencies.

[0058] Another example of noise reduction using difference values ​​is edge smoothing, where the difference values ​​identify edge regions of an image, and typically the pixel difference values ​​in edge regions are relatively large. Specific treatments are applied to the identified edge regions, for example, to reduce the intensity of noise reduction in these regions to avoid excessive smoothing. There are various methods for noise reduction based on difference values, and are not limited to the above examples. This application does not limit the specific implementation methods of noise reduction using difference values.

[0059] In the above embodiment, by indirectly introducing Y channel information from the source image using an image of the target format, the similarity between adjacent region pixels can be effectively distinguished, color noise in the edge region can be reduced to the greatest extent possible, and the problems of color bleeding and residual edge noise in color noise reduction can be solved.

[0060] In one embodiment of this application, the step of performing noise reduction on the source image based on the difference value distribution includes the steps of calculating a weight parameter for each pixel point in the source image based on the corresponding pixel difference value in the difference value distribution for each pixel point in the source image, and realizing noise reduction by weighting adjacent pixels corresponding to each pixel point in the source image based on the weight parameter of each pixel point.

[0061] There is a one-to-one correspondence between pixel points in the source image and pixel points in the target image; that is, the pixel difference value of a certain pixel point in the target image is also the pixel difference value of the corresponding pixel point in the source image.

[0062] For each pixel point in the source image, the corresponding pixel difference value in the difference value distribution is obtained, a weight parameter for that pixel is calculated based on the obtained pixel difference value, adjacent pixels are weighted based on the weight parameter of that pixel, and the weighted pixel value is used as the pixel value of that pixel, thereby achieving noise reduction.

[0063] For example, for each pixel point, the difference in pixel value between it and all pixel points in the 2x2 region adjacent to its left is calculated. Assuming the image is 8x8, the pixel difference value between the pixel point at position (4,4) in the source image and the pixel point at position (4,4) in the target image is obtained, and it is assumed that the difference value is (4,5,6,7). The difference values ​​are converted into weight parameters. In this application, there are various and not limited methods for obtaining the weight parameters; for example, it may be a normalization transformation or a scaling transformation. For example, in this application, a scaling transformation is used to convert the difference values ​​into weights (0.182, 0.227, 0.273, 0.318). After obtaining the weight parameters, assuming that the pixel values ​​of the pixel points at positions (3,4), (2,4), (3,5), and (2,5) in the source image are (100, 150, 200, and 300), respectively, the weights are applied to 100 × 0.182 + 150 × 0.227 + 200 × 0.273 + 300 × 0.318, resulting in a pixel value of 202 for the pixel point at position (4,4) in the source image. Noise reduction for a pixel point is achieved by calculating the pixel value of a pixel point by weighting the adjacent pixel points to that pixel point.

[0064] In the above embodiment, the target image is weighted as a guide image for the source image, and the Y channel information of the source image is indirectly introduced by the target image. This makes it possible to effectively distinguish the similarity between adjacent region pixels and to reduce color noise to the maximum extent possible in the edge region, without color bleeding or residual edge noise.

[0065] In one embodiment of this application, the step of performing noise reduction on the source image based on the difference value distribution includes the steps of obtaining a weight matrix for each pixel point in the source image and achieving noise reduction by weighting the pixel values ​​of all pixel points in the adjacent regions corresponding to each pixel point in the source image based on the weight matrix of the pixel points, wherein the weight matrix is ​​calculated from the difference value matrix of the corresponding pixel points in the target image.

[0066] In an embodiment where the adjacent region of the above-mentioned pixel point is a rectangular region within a predetermined radius centered on the pixel point, and the difference matrix between the pixel point and the adjacent region is calculated:

[0067] The difference matrix of each pixel point in the target image is converted into a weight matrix using equation 5 below.

[0068] W(x,y,k1,k2)=exp(-Diff(x,y,k1,k2) / Sigma) Equation 5

[0069] Exp represents the exponent of e, Sigma represents a predetermined constant, and W(x,y,k1,k2) represents the weight matrix of the pixel point located at (x,y).

[0070] For each pixel point in the source image, a corresponding weight matrix is ​​obtained based on the correspondence between the pixel points. Based on the weight matrix of the pixel, all pixel points in the adjacent region of the pixel are weighted, and the weighted pixel values ​​are used as the pixel values ​​of the pixel point, thereby achieving noise reduction.

[0071] In the above embodiment, by constructing a weight matrix using the difference value distribution, noise reduction can be performed on the pixel points by making full use of the surrounding region of the pixel points, thereby improving the effect of noise reduction.

[0072] Furthermore, the step of performing noise reduction on the source image based on the difference value distribution includes performing noise reduction on the U channel and the V channel, respectively, based on the difference value distribution.

[0073] The calculated weights are used to assign weights to the adjacent region pixels in the U and V channels, specifically using equations 6 and 7.

[0074]

number

[0075]

number

[0076] NRU is the result of noise reduction applied to the U channel in the YUV image, and NRV is the result of noise reduction applied to the V channel in the YUV image. U(x+k1,y+k2) represents adjacent pixels in the U channel centered at (x,y) within a radius r, and V(x+k1,y+k2) represents adjacent pixels in the V channel centered at (x,y) within a radius r.

[0077] As shown in Figure 2, Figure 2 is a block diagram of the configuration of a color noise reduction device 200 according to an embodiment of the present application. The color noise reduction device 200 comprises a conversion module 210, a calculation module 220, and a noise reduction module 230.

[0078] The conversion module 210 is configured to convert a source image in YUV format to a target image in a target format, which is a color encoding format that does not use ruma for encoding.

[0079] The calculation module 220 is configured to calculate the pixel difference value between adjacent pixel points in the target image and to obtain the difference value distribution.

[0080] The noise reduction module 230 is configured to perform noise reduction on the source image based on the difference value distribution.

[0081] In one selectable embodiment of the present application, the calculation module 220 is configured to calculate the pixel difference value between a pixel point in the target image and all pixel points in the adjacent region of the pixel point, obtain a difference value matrix, obtain the difference value matrix for all pixel points in the target image, and obtain the difference value distribution.

[0082] In one selectable embodiment of this application, the adjacent region is a region in the target image within a predetermined radius centered on the pixel point.

[0083] In one selectable embodiment of this application, the noise reduction module 230 is configured to calculate a weight parameter for each pixel point in the source image based on the corresponding pixel difference value in the difference value distribution for each pixel point, and to achieve noise reduction by weighting the adjacent pixels corresponding to each pixel point in the source image based on the weight parameter for each pixel point.

[0084] In one selectable embodiment of this application, the noise reduction module 230 is configured to obtain a weight matrix for each pixel point in the source image, and to perform noise reduction by weighting all pixel points in the adjacent region corresponding to each pixel point in the source image based on the weight matrix for each pixel point, wherein the weight matrix is ​​calculated from the difference matrix of the corresponding pixel points in the target image.

[0085] In one selectable embodiment of this application, the target format includes the RGB format.

[0086] In one selectable embodiment of this application, the noise reduction module 230 is specifically configured to perform noise reduction on the U channel and the V channel, respectively, based on the difference value distribution.

[0087] Figure 3 is a block diagram of the configuration of an electronic device 300 according to an embodiment of this application. As shown in Figure 3, the electronic device 300 comprises a processor 310 and a memory 320.

[0088] The processor 310, the memory 320, and each element are electrically connected directly or indirectly to enable data transmission or exchange. For example, these elements are electrically connected by one or more communication buses or signal lines. The memory 320 is configured to store computer programs, and for example, the software function module shown in Figure 2, namely the color noise reduction device 200, is stored in the memory 320. The color noise reduction device 200 comprises at least one software function module stored in the memory 320 in the form of software or firmware, or incorporated into the operating system (OS) of the electronic device 300. The processor 310 is configured to execute executable modules stored in the memory 320, such as the software function module or computer program of the color noise reduction device 200.

[0089] Memory 320 may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), or Electric Erasable Programmable Read-Only Memory (EEPROM).

[0090] The processor 310 may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor including a Central Processing Unit (CPU), a Network Processor (NP), a microprocessor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of realizing or executing each method, step and logic block disclosed in the embodiments of this application. Alternatively, the processor 310 may be any conventional processor.

[0091] Embodiments of this application further provide a computer-readable non-volatile storage medium (hereinafter abbreviated as "storage medium"). A computer program is stored in the storage medium, and when the computer program is executed by a computer, for example, the electronic device 300 described above, the color noise reduction method described above is performed.

[0092] Each example in this specification is described progressively, with emphasis on describing the differences between each example and the similarities between them, which can be discussed through cross-referencing.

[0093] In some embodiments of this application, the described apparatus and methods can be implemented in other ways. The embodiments of the apparatus described above are illustrative only. For example, the flowcharts and block diagrams in the drawings illustrate implementable architectures, functions, and operations based on the apparatus, methods, and computer program products according to some embodiments of this application, where each block in the flowchart or block diagram can represent a module, program segment, or part of code containing one or more executable commands capable of implementing a predetermined logical function. In some interchangeable implementations, the implementation of the functions described in the blocks may differ from the order shown in the drawings. For example, two consecutive blocks may actually be executed almost in parallel, or in some cases in reverse order. This is determined by the required functions. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated, hardware-based system that performs a predetermined function or operation, or by a combination of dedicated hardware and computer commands.

[0094] Furthermore, each functional module in each embodiment of this application may be formed by integrating them into a single independent part, each module may exist independently, or it may be formed by integrating two or more modules into a single independent part.

[0095] The aforementioned functions can be implemented in the form of software function modules, which, when sold or used as independent products, can be stored on a single computer-readable medium. Under this understanding, the technical proposal of this application, or any part thereof that can contribute to the prior art, or any part thereof, can be implemented in the form of a software product. This computer software product is stored on a computer-readable medium and includes a number of commands for a computer device (which may be a personal computer, laptop computer, server, or electronic device) to perform all or part of the steps of the methods according to each embodiment of this application. The computer-readable medium includes various media capable of storing program code, such as USB disks, portable hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The above are merely specific embodiments of the present application, and the scope of protection of this application is not limited thereto. A person skilled in the art will know that any changes or substitutions made within the scope of the art disclosed in this application are also within the scope of protection of this application. Therefore, the scope of protection of this application is equivalent to the claims.

Claims

1. The process involves converting a source image in YUV format to a target image in a target format, which is a color encoding format that does not use ruma for encoding. The steps include calculating the pixel difference values ​​between adjacent pixel points in the target image and obtaining the difference value distribution, The step includes performing noise reduction on the source image based on the difference value distribution. A method for reducing color noise characterized by the following features.

2. The step of performing noise reduction on the source image based on the difference value distribution is: The steps include: calculating a weight parameter for each pixel point in the source image based on the corresponding pixel difference value in the difference value distribution for each pixel point; The step of achieving noise reduction by weighting adjacent pixels corresponding to each pixel point in the source image based on the weight parameter of each pixel point. The color noise reduction method according to feature 1.

3. The step of calculating the pixel difference value between adjacent pixel points in the target image and obtaining the difference value distribution is: The steps include: calculating the pixel difference value between a pixel point in the target image and all pixel points in the adjacent region of that pixel point, and obtaining a difference value matrix; The step of obtaining a difference value matrix for all pixel points in the target image and obtaining the difference value distribution. The color noise reduction method according to feature 1.

4. The adjacent region is a region within a predetermined radius centered on the pixel point in the target image. The color noise reduction method according to feature 3.

5. The step of performing noise reduction on the source image based on the difference value distribution is: The steps include obtaining the weight matrix for each pixel point in the source image, The step of achieving noise reduction by weighting all pixel points in the adjacent region corresponding to each pixel point in the source image based on the weight matrix of each pixel point, The weight matrix is ​​calculated using the difference matrix of corresponding pixel points in the target image. The color noise reduction method according to feature 3 or 4.

6. The aforementioned target format includes the RGB format. The color noise reduction method according to any one of claims 1 to 4.

7. The step of performing noise reduction on the source image based on the difference value distribution is: This includes performing noise reduction on the U channel and V channel, respectively, based on the difference value distribution. The color noise reduction method according to any one of claims 1 to 4.

8. A conversion module configured to convert a source image in YUV format to a target image in a target format that is a color encoding format that does not use ruma for encoding, A calculation module configured to calculate the pixel difference value between adjacent pixel points in the target image and obtain the difference value distribution, The system includes a noise reduction module configured to perform noise reduction on the source image based on the difference value distribution. A color noise reduction device characterized by the following features.

9. The system comprises a processor and memory, the processor and memory communicate via a bus, the memory stores program commands executable by the processor, and when the processor reads the program commands, the color noise reduction method described in any one of claims 1 to 7 is executed. An electronic device characterized by the following features.

10. A computer-readable medium, A computer program command is stored in the computer-readable medium, and when the computer program command is read and executed by a computer, the color noise reduction method according to any one of claims 1 to 7 is performed. A computer-readable medium characterized by the following:

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