Image noise reduction processing method, device, equipment and storage medium

Abstract

The present application provides an image denoising method, device, equipment and storage medium, belonging to the field of image processing technology, the method comprising: determining a first image to be subjected to denoising; determining the noise level of the first image and the gradient value of each pixel in the first image; determining a gradient threshold according to the noise level of the first image; determining a diffusion function corresponding to each pixel in the first image according to the gradient value of each pixel in the first image and the gradient threshold; performing denoising on the first image according to the diffusion function corresponding to each pixel in the first image to obtain a target image. The step effect in the target image is avoided, and the accuracy of the denoising is improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to an image noise reduction processing method, device, equipment and storage medium. Background Art

[0002] A noisy image refers to an image with redundant interference information. The presence of this interference information seriously affects the quality of the image. In order to improve the quality of the image, the image needs to be denoised.

[0003] In the related art, partial differential equations can be used to perform denoising on images. However, the target image obtained by the existing partial differential equation processing will produce an obvious step effect and will also produce new features in the target image, seriously affecting the quality of the target image. That is, the accuracy of the existing partial differential equation denoising processing is low. Summary of the invention

[0004] In order to avoid the step effect during the noise reduction process and affect the accuracy of the noise reduction process, the present application provides an image noise reduction method, device, equipment and storage medium, which can improve the accuracy of the noise reduction process.

[0005] In a first aspect, the present application provides an image noise reduction processing method, comprising:

[0006] Determining a first image to be subjected to noise reduction processing;

[0007] determining a noise level of the first image and a gradient value of each pixel in the first image;

[0008] determining a gradient threshold according to a noise level of the first image;

[0009] Determining a diffusion function corresponding to each pixel in the first image according to a gradient value of each pixel in the first image and the gradient threshold;

[0010] The first image is subjected to noise reduction processing according to the diffusion function corresponding to each pixel in the first image to obtain a target image.

[0011] In a possible implementation, for any pixel in the first image, determining a diffusion function corresponding to the pixel according to a gradient value of the pixel and the gradient threshold includes:

[0012] determining a first threshold and a second threshold;

[0013] A diffusion function corresponding to the pixel is determined according to the gradient value of the pixel, the gradient threshold, the first threshold, and the second threshold.

[0014] In a possible implementation, determining the diffusion function corresponding to the pixel according to the gradient value of the pixel, the gradient threshold, the first threshold, and the second threshold includes:

[0015] The diffusion function g(r) corresponding to the pixel is determined according to the following formula 1:

[0016]

[0017] in, The r is the modulus of the gradient of the pixel, the k is the gradient threshold, the s is the first threshold, and the h is the second threshold.

[0018] In a possible implementation, performing noise reduction processing on the first image according to a diffusion function corresponding to each pixel in the first image to obtain a target image includes:

[0019] Determine a two-dimensional matrix corresponding to the first image;

[0020] The target image is obtained by performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function.

[0021] In a possible implementation, performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain the target image includes:

[0022] Performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain a second image;

[0023] The second image is sharpened to obtain the target image.

[0024] In a possible implementation, determining a two-dimensional matrix corresponding to the first image includes:

[0025] Obtaining a pixel value of each pixel in the first image;

[0026] Convert the pixel value of each pixel in the first image into a floating point number to obtain a floating point number corresponding to each pixel in the first image;

[0027] A two-dimensional matrix corresponding to the first image is determined according to floating-point numbers corresponding to each pixel in the first image.

[0028] In a possible implementation manner, determining the noise level of the first image includes:

[0029] Determine the high pass filter;

[0030] The first image is processed by the high-pass filter to obtain a noise level of the first image.

[0031] In a second aspect, the present application provides an image noise reduction processing device, including a first determination module, a second determination module, a third determination module, a fourth determination module and a noise reduction module, wherein:

[0032] The first determination module is used to determine a first image to be subjected to noise reduction processing;

[0033] The second determination module is used to determine the noise level of the first image and the gradient value of each pixel in the first image;

[0034] The third determination module is used to determine a gradient threshold according to a noise level of the first image;

[0035] The fourth determination module is used to determine the diffusion function corresponding to each pixel in the first image according to the gradient value of each pixel in the first image and the gradient threshold;

[0036] The noise reduction module is used to perform noise reduction processing on the first image according to the diffusion function corresponding to each pixel in the first image to obtain a target image.

[0037] In a possible implementation, for any pixel in the first image, the fourth determination module is specifically configured to:

[0038] determining a first threshold and a second threshold;

[0039] A diffusion function corresponding to the pixel is determined according to the gradient value of the pixel, the gradient threshold, the first threshold, and the second threshold.

[0040] In a possible implementation manner, the fourth determining module is specifically configured to:

[0041] The diffusion function g(r) corresponding to the pixel is determined according to the following formula 1:

[0042]

[0043] in, The r is the modulus of the gradient of the pixel, the k is the gradient threshold, the s is the first threshold, and the h is the second threshold.

[0044] In a possible implementation manner, the noise reduction module is specifically used to:

[0045] Determine a two-dimensional matrix corresponding to the first image;

[0046] The target image is obtained by performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function.

[0047] In a possible implementation manner, the noise reduction module is specifically used to:

[0048] Performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain a second image;

[0049] The second image is sharpened to obtain the target image.

[0050] In a possible implementation manner, the noise reduction module is specifically used to:

[0051] Obtaining a pixel value of each pixel in the first image;

[0052] Convert the pixel value of each pixel in the first image into a floating point number to obtain a floating point number corresponding to each pixel in the first image;

[0053] A two-dimensional matrix corresponding to the first image is determined according to floating-point numbers corresponding to each pixel in the first image.

[0054] In a possible implementation manner, the second determining module is specifically configured to:

[0055] Determine the high pass filter;

[0056] The first image is processed by the high-pass filter to obtain a noise level of the first image.

[0057] In a third aspect, the present application provides an image noise reduction processing device, including: a processor and a memory;

[0058] The memory stores computer-executable instructions;

[0059] The processor executes the computer execution command stored in the memory, so that the processor performs the image noise reduction processing method as described in any one of the first aspects.

[0060] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the image denoising processing method described in any one of the first aspects.

[0061] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, can implement the image denoising processing method described in any one of the first aspects.

[0062] The present application provides an image denoising method, device, equipment and storage medium, which first determines a first image to be subjected to denoising, then determines the noise level of the first image and the gradient value of each pixel in the first image, and determines the gradient threshold according to the noise level of the first image; then determines the diffusion function corresponding to each pixel in the first image according to the gradient value and the gradient threshold of each pixel in the first image; finally, denoises the first image according to the diffusion function corresponding to each pixel in the first image to obtain a target image. The diffusion function of the present application can adaptively adjust the degree of image smoothing, and the target image will not produce a step effect, nor will it create new features in the image, thereby improving the accuracy of denoising. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0064] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0065] Figure 2 A schematic diagram of a method for image noise reduction processing provided in an embodiment of the present application Figure 1 ;

[0066] Figure 3 A curve diagram showing the relationship between the image noise level and the image resolution provided in the embodiment of the present application;

[0067] Figure 4 A curve diagram showing the relationship between the noise level and the gradient threshold provided in the embodiment of the present application;

[0068] Figure 5 A schematic diagram of a method for image noise reduction processing provided in an embodiment of the present application Figure 2 ;

[0069] Figure 6 A graph of the φ(r) function provided in an embodiment of the present application;

[0070] Figure 7 A graph of the optimized φ(r) function provided in an embodiment of the present application;

[0071] Figure 8 A schematic diagram of the structure of an image noise reduction processing device provided in an embodiment of the present application;

[0072] Fig. 9 A schematic diagram of the structure of an image noise reduction processing device provided in an embodiment of the present application.

[0073] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in combination with the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0075] For ease of understanding, the following Figure 1 , the application scenarios to which the embodiments of the present application are applicable are described.

[0076] Figure 1 This is a schematic diagram of an application scenario provided by the embodiment of this application. Figure 1 The terminal device may be provided with a noise reduction processing module. When the terminal device needs to perform noise reduction processing on a noisy image, the terminal device may first obtain the noisy image, and then perform noise reduction processing on the noisy image through the noise reduction processing module to obtain a target image.

[0077] In the related art, the noise reduction processing module of the terminal device performs processing through the Perno-Malik (PM) equation in Formula 2.

[0078]

[0079] Where I0 represents the original image; (x, y) represents the spatial coordinates of the image; the variable t represents the parameter of the processing order, and represents the number of iterations in the discrete process; I(x, y, t) represents the image after the tth iteration; div represents the divergence operator; represents the gradient operator; represents the magnitude of the image gradient, It is a diffusion function, which is equivalent to an edge detection operator. It is used to control the diffusion speed, which is inversely proportional to the image gradient. It performs fast anisotropic diffusion in the area where the image changes smoothly, and performs slow diffusion or even no diffusion in the edge area of ​​the image, so as to achieve the effect of reducing noise while maintaining edge information. Usually as shown in Formula 3, in order to simplify the formula, r is used instead of

[0080]

[0081] In the diffusion function, K is the gradient threshold.

[0082] When Formula 2 is a one-dimensional case, the image I iterated t times t It can be expressed as formula 4:

[0083]

[0084] Among them, I x represents the first-order gradient; I xx represents the second-order gradient; φ(r)=rg(r), then

[0085]

[0086] It can be seen from Formula 5 that in the flat area, r<K, the image diffuses forward and the noise can be smoothed; near the edge, r>K, the image diffuses backward and the edge is sharpened. This shows that the selection of K value is crucial to the smoothness of the image. The larger the K value, the higher the image smoothness. However, the specific image is polluted to different degrees by noise. A fixed K value will lead to insufficient smoothing of images with severe noise pollution and excessive smoothing of images with less severe pollution. This will lead to a serious staircase effect and may also generate new features in the image, seriously affecting the quality of the target image. That is, the accuracy of the existing partial differential equation denoising process is low.

[0087] In order to solve the above technical problems, the present application proposes a new diffusion function, which can adaptively control the diffusion mode according to the image features, effectively remove the noise of the image, and will not produce a step effect, thereby improving the accuracy of the noise reduction processing.

[0088] The technical solutions shown in the present application are described in detail below through specific embodiments. It should be noted that the following embodiments may exist independently or in combination with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0089] Figure 2 A schematic diagram of a method for image noise reduction processing provided in an embodiment of the present application Figure 1 . See Figure 2 , the method may include:

[0090] S201: Determine a first image to be subjected to noise reduction processing.

[0091] The execution subject of the present application may be a terminal device, or an image noise reduction processing device arranged in the terminal device. The image noise reduction processing device may be implemented by software, or by a combination of software and hardware.

[0092] The first image may be a noisy image.

[0093] S202: Determine a noise level of the first image and a gradient value of each pixel in the first image.

[0094] The noise level may refer to the noise level of the first image, and one image corresponds to one noise level.

[0095] The noise level may refer to the degree to which an image is contaminated by noise, and the wavelet algorithm may be used to estimate the noise level of an image.

[0096] The noise level of the first image can be determined as follows:

[0097] Determine a high-pass filter; and process the first image through the high-pass filter to obtain a noise level of the first image.

[0098] For the same image, the noise levels calculated at different resolutions are different. For images with different resolutions, their noise levels are counted and the relationship between resolution (image width, ny) and noise is fitted to obtain Figure 3 , Figure 3 The relationship curve diagram between the image noise level and the image resolution provided in the embodiment of the present application, and the noise level correction function is shown in Formula 6.

[0099]

[0100] Where N represents the noise level; ny represents the image width; e represents a natural constant, which is an infinite non-repeating decimal and a transcendental number, and its value is approximately 2.718281828459045.

[0101] Among them, r x Represents the gradient of the pixel in the x direction, r y Represents the gradient of the pixel in the y direction. The gradient of the pixel in the x direction is equivalent to the pixel difference between two adjacent pixels in the x direction. The gradient of the pixel in the y direction is equivalent to the pixel difference between two adjacent pixels in the y direction.

[0102] When the first image is represented by a two-dimensional discrete function, the gradient of each pixel in the first image is the derivative of the two-dimensional discrete function.

[0103] S203: Determine a gradient threshold according to the noise level of the first image.

[0104] The noise level can be modulated through experiments to fit the appropriate gradient threshold range. The modulated curve is as follows: Figure 4 As shown, Figure 4 The relationship curve diagram between the noise level and the gradient threshold provided in the embodiment of the present application is expressed as the function of Formula 7.

[0105] K=1.7714*N 3 -11.5143*N 2 +18.7429*N Formula 7

[0106] Where N represents the noise level and K represents the gradient threshold.

[0107] For some images with strong noise, when the image width ny is less than 480 and the noise level is greater than 2, K can be set to 10.

[0108] S204: Determine a diffusion function corresponding to each pixel in the first image according to the gradient value and the gradient threshold of each pixel in the first image.

[0109] For any pixel in the first image, the diffusion function corresponding to the pixel can be determined according to the gradient value and the gradient threshold of the pixel in the following manner:

[0110] Determine a first threshold and a second threshold; and determine a diffusion function corresponding to the pixel according to the gradient value of the pixel, the gradient threshold, the first threshold, and the second threshold.

[0111] The first threshold may be a smoothing threshold, and the image may be divided into two parts according to the first threshold and the second threshold, and each part corresponds to a different diffusion function.

[0112] S205 , performing noise reduction processing on the first image according to the diffusion function corresponding to each pixel in the first image to obtain a target image.

[0113] The target image may be a denoised image.

[0114] The first image can be subjected to denoising according to a diffusion function corresponding to each pixel in the first image to obtain a target image in the following manner: determining a two-dimensional matrix corresponding to the first image; and performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain the target image.

[0115] exist Figure 2 In the embodiment shown, the first image to be subjected to denoising is first determined, then the noise level of the first image and the gradient value of each pixel in the first image are determined, and the gradient threshold is determined according to the noise level of the first image; then the diffusion function corresponding to each pixel in the first image is determined according to the gradient value and the gradient threshold of each pixel in the first image; finally, the first image is subjected to denoising according to the diffusion function corresponding to each pixel in the first image to obtain the target image. By estimating the degree of noise pollution of the image, the degree of image smoothing is adaptively adjusted, and the image noise fitting K value is used to ensure that the denoised image will not be insufficiently smoothed (noise remains) or smoothly transitioned, thereby improving the accuracy of denoising.

[0116] Based on any of the above embodiments, Figure 6 The embodiment shown is used to explain the above-mentioned image noise reduction processing method in detail.

[0117] Figure 5 A schematic diagram of a method for image noise reduction processing provided in an embodiment of the present application Figure 2 . See Figure 5 , the method may include:

[0118] S501: Determine a first image to be subjected to noise reduction processing.

[0119] It should be noted that the execution process of S501 can refer to the execution process of S201, which will not be described in detail here.

[0120] S502: Determine the noise level of the first image and the gradient value of each pixel in the first image.

[0121] It should be noted that the execution process of S502 can refer to the execution process of S202, which will not be described in detail here.

[0122] S503: Determine a gradient threshold according to the noise level of the first image.

[0123] It should be noted that the execution process of S503 can refer to the execution process of S203, which will not be described in detail here.

[0124] S504: Determine a first threshold and a second threshold.

[0125] It should be noted that the execution process of S504 can refer to the execution process of S204, which will not be described in detail here.

[0126] S505 : Determine a diffusion function corresponding to the pixel according to the gradient value of the pixel, the gradient threshold, the first threshold, and the second threshold.

[0127] The diffusion function g(r) corresponding to the pixel can be determined according to the following formula:

[0128]

[0129] in, The r is the modulus of the gradient of the pixel, the k is the gradient threshold, the s is the first threshold, and the h is the second threshold.

[0130] For ease of understanding, the method for determining g(r) when s≤r≤h is explained in detail below.

[0131] With diffusion function Determine φ(r) = rg(r) and draw a graph of the φ(r) function, as shown below: Figure 6 shown.

[0132] According to formula 4, when the gradient changes with time, there is the following formula 8:

[0133]

[0134] Among them, I x is the first-order gradient; I xx is the second-order gradient; I xxx is a third-order gradient.

[0135] Hypothesis I x >0, then I xxx <0, from Figure 6 It can be seen that:

[0136] When r≤s, φ(r) increases with r and the function is a convex function, then φ′>0, φ″<0, φ″Ixx 2 <0,φ′I xxx <0, then A negative number will give the desired smoothing effect.

[0137] When s≤r≤ip, φ(r) decreases and the function is convex, then φ′<0, φ″<0, φ″I xx 2 <0,φ′I xxx >0, then If it is a positive or negative number, the result is uncontrollable.

[0138] The maximum gradient occurs at the inflection point (ip) of the edge, where I xx =0,I xxx <0, so The sign of is opposite to that of φ′.

[0139] When r>ip, φ(r) decreases and the function is a concave function, then φ′<0, φ″>0, φ″I xx 2 >0,φ′I xxx >0, then is a positive number, the image is sharpened. However, when the gradient value increases, This causes points with lower gradient amplitudes to be enhanced to a greater extent than points with higher gradient amplitudes, ultimately causing edge degradation.

[0140] In summary, when r>s, the diffusion function will produce some unexpected phenomena. In order to improve the robustness of the diffusion function, the function φ needs to meet the following conditions:

[0141] (a) When r<s, φ increases and the function is convex;

[0142] (b) When r>s, φ decreases and the function is concave;

[0143] (c) When r>s, the point with larger gradient corresponds to It should not be reduced;

[0144] by The determined function φ satisfies the first condition, but cannot satisfy conditions (b) and (c) simultaneously, unless φ″ is 0. Therefore, when r>s, φ is set to a linear function, φ″ is 0, and Formula 8 becomes The sign of is opposite to that of φ′.

[0145] The final diffusion function is shown in Formula 1, where s is a constant. When r = s, The value is the largest, h is also a constant, which determines the slope of the linear function when r>s. The larger h is, The larger the value, the higher the degree of sharpening. The curve of the φ(r) function determined by the diffusion function of formula 1 is as follows Figure 7 shown.

[0146] S506: Determine a two-dimensional matrix corresponding to the first image.

[0147] The two-dimensional matrix corresponding to the first image can be determined in the following way:

[0148] Obtain a pixel value of each pixel in the first image; convert the pixel value of each pixel in the first image into a floating-point number to obtain a floating-point number corresponding to each pixel in the first image; and determine a two-dimensional matrix corresponding to the first image based on the floating-point number corresponding to each pixel in the first image.

[0149] S507 , performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain a second image.

[0150] The second image can be obtained by performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function in the following manner: first, the image is subjected to one-dimensional diffusion processing in rows according to the diffusion function to obtain an intermediate image, and then the intermediate image is subjected to one-dimensional diffusion processing in columns to obtain the second image.

[0151] Specifically, the diffusion function can be substituted into the PM equation in formula 2, and the second image can be obtained by solving the PM equation.

[0152] For programming efficiency, the multiplication operator splitting algorithm can be selected, and the Tomas pursuit method can be used to quickly solve the PM equation.

[0153] The algebraic equation satisfied is:

[0154]

[0155] in and represents the matrix corresponding to row processing and column processing, and τ represents the processing step size. and They are all N*N tridiagonal matrices, It is also a tridiagonal matrix of a, b, c, which can be expressed as follows:

[0156]

[0157] in

[0158] Where g is the formula 1. The matrices of s and g can be calculated by given K and h and the modulus r of the gradient. The subscript i of g is the column index and k is the row index. After obtaining the matrix of g, we can get The tridiagonal matrix of .

[0159] The Tomas algorithm decomposes the original equation Ax=d into two steps, first solving Ly=d, then solving Rx=y, that is, decomposing the coefficient A of the original equation into L and R. Since Tomas is mainly used to solve tridiagonal matrices, A must be a diagonally dominant tridiagonal matrix. Formula 9 can be abstracted to solve the tridiagonal matrix equation shown below

[0160]

[0161] Now decompose A into the product of a lower triangular matrix L with 1 on the main diagonal and another right triangular matrix R

[0162] A=LR

[0163]

[0164] Tomas calculation steps are as follows:

[0165] Decompose A into LR;

[0166] First solve Ly = d, where d is the original image, find the matrix of y, and get the coefficients of l, m, and r;

[0167] Initialization: m1 = a1, r1 = β1, y1 = d1,

[0168] Forward Substitution

[0169] For i=2...N

[0170] r i =β i

[0171] l i =ci / m i-1 =r i-1 / m i-1

[0172] m i =a i -l i *r i-1

[0173] y i =d i -l i *y i-1

[0174] End

[0175] Substituting in reverse, solve Rx=y

[0176] Initialization: i = N, x n =y n / m n

[0177] For i=N-1…1

[0178] x i =y i -β i x i+1 / m i

[0179] end

[0180] Get the first Tomas calculation of the intermediate image.

[0181] Given the diffusion function g(r) matrix, the triangular matrix of row diffusion can be obtained according to Formula 10 and Formula 11. Then, according to the Tomas algorithm, the triangular matrix is ​​decomposed into LR. Using the Tomas algorithm process, first forward substitute to obtain all the coefficients l, m, r of LR, and the solution y of the intermediate equation, and then reverse substitute to obtain the matrix corresponding to the intermediate image of the line diffusion result.

[0182] According to the PM equation and the results of row diffusion, the Tomas algorithm is used to decompose the matrix in the column direction. Similar to the steps in the row direction, first perform forward substitution to calculate all coefficients and y, and then perform reverse substitution to obtain the matrix corresponding to the second image of the column diffusion result.

[0183] S508: Perform sharpening processing on the second image to obtain a target image.

[0184] The second image can be sharpened using the USM algorithm.

[0185] exist Figure 5In the embodiment shown, the first image to be subjected to denoising is first determined, then the noise level of the first image and the gradient value of each pixel in the first image are determined, and the gradient threshold is determined according to the noise level of the first image; the first threshold and the second threshold are determined; the diffusion function corresponding to the pixel is determined according to the gradient value, the gradient threshold, the first threshold and the second threshold of the pixel; the two-dimensional matrix corresponding to the first image is determined, and the two-dimensional matrix is ​​subjected to row diffusion processing and column diffusion processing according to the diffusion function to obtain the second image; finally, the second image is sharpened to obtain the target image. By estimating the degree of noise pollution of the image, the degree of image smoothing is adaptively adjusted, and the image noise fitting K value is used to ensure that the denoised image will not be insufficiently smoothed (noise remains) or smoothly transitioned, thereby improving the accuracy of the denoising process.

[0186] By comparing a series of objective evaluation criteria and subjective quality evaluation criteria of human eyes, such as peak signal-to-noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), etc., it is confirmed that the performance of the segmented diffusion function of the present application is higher than that of the diffusion function without segmentation. The diffusion function of the present application can adaptively adjust the degree of image smoothing, and the target image will not produce a step effect, nor will it create new features in the image, thereby improving the accuracy of the noise reduction process.

[0187] Figure 8 This is a schematic diagram of the structure of an image noise reduction processing device provided in an embodiment of the present application. Figure 8 The image noise reduction processing device 10 includes a first determination module 11, a second determination module 12, a third determination module 13, a fourth determination module 14 and a noise reduction module 15, wherein:

[0188] The first determination module 11 is used to determine a first image to be subjected to noise reduction processing;

[0189] The second determination module 12 is used to determine the noise level of the first image and the gradient value of each pixel in the first image;

[0190] The third determination module 13 is used to determine a gradient threshold according to a noise level of the first image;

[0191] The fourth determination module 14 is used to determine the diffusion function corresponding to each pixel in the first image according to the gradient value of each pixel in the first image and the gradient threshold;

[0192] The noise reduction module 15 is used to perform noise reduction processing on the first image according to the diffusion function corresponding to each pixel in the first image to obtain a target image.

[0193] In a possible implementation manner, for any pixel in the first image, the fourth determining module 14 is specifically configured to:

[0194] determining a first threshold and a second threshold;

[0195] A diffusion function corresponding to the pixel is determined according to the gradient value of the pixel, the gradient threshold, the first threshold, and the second threshold.

[0196] In a possible implementation manner, the fourth determining module 14 is specifically configured to:

[0197] The diffusion function g(r) corresponding to the pixel is determined according to the following formula 1:

[0198]

[0199] in, The r is the modulus of the gradient of the pixel, the k is the gradient threshold, the s is the first threshold, and the h is the second threshold.

[0200] In a possible implementation manner, the noise reduction module 15 is specifically used to:

[0201] Determine a two-dimensional matrix corresponding to the first image;

[0202] The target image is obtained by performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function.

[0203] In a possible implementation manner, the noise reduction module 15 is specifically used to:

[0204] Performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain a second image;

[0205] The second image is sharpened to obtain the target image.

[0206] In a possible implementation manner, the noise reduction module 15 is specifically used to:

[0207] Obtaining a pixel value of each pixel in the first image;

[0208] Convert the pixel value of each pixel in the first image into a floating point number to obtain a floating point number corresponding to each pixel in the first image;

[0209] A two-dimensional matrix corresponding to the first image is determined according to floating-point numbers corresponding to each pixel in the first image.

[0210] In a possible implementation manner, the second determining module 12 is specifically configured to:

[0211] Determine the high pass filter;

[0212] The first image is processed by the high-pass filter to obtain a noise level of the first image.

[0213] The image noise reduction processing device 10 provided in the present application can execute the technical solution shown in the above-mentioned image noise reduction processing method embodiment. Its implementation principle and beneficial effects are similar and will not be described in detail here.

[0214] Fig. 9 This is a schematic diagram of the structure of an image noise reduction processing device provided in an embodiment of the present application. Fig. 9 The image noise reduction processing device 20 includes: a memory 21 and a processor 22. Exemplarily, the memory 21 and the processor 22 are connected to each other via a bus 23.

[0215] The memory 21 stores computer-executable instructions;

[0216] The processor 22 executes the computer-executable instructions stored in the memory 21, so that the processor 22 performs any one of the above-mentioned image noise reduction processing methods.

[0217] Fig. 9 The image noise reduction processing device shown in the illustrated embodiment can execute the technical solution shown in the above-mentioned image noise reduction processing method embodiment, and its implementation principle and beneficial effects are similar, which will not be repeated here.

[0218] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement any of the aforementioned image noise reduction processing methods.

[0219] The embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, can implement the above-mentioned image noise reduction processing method.

[0220] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0221] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. An image noise reduction processing method, characterized in that: include: Determining a first image to be subjected to noise reduction processing; determining a noise level of the first image and a gradient value of each pixel in the first image; determining a gradient threshold according to a noise level of the first image; Determining a diffusion function corresponding to each pixel in the first image according to a gradient value of each pixel in the first image and the gradient threshold; Performing noise reduction processing on the first image according to a diffusion function corresponding to each pixel in the first image to obtain a target image; For any pixel in the first image; Determining a diffusion function corresponding to the pixel according to the gradient value of the pixel and the gradient threshold, comprising: determining a first threshold and a second threshold; The diffusion function g(r) corresponding to the pixel is determined according to the following formula 1: in, The r is the modulus of the gradient of the pixel, the k is the gradient threshold, the s is the first threshold, and the h is the second threshold.

2. The method according to claim 1, characterized in that: The first image is subjected to noise reduction processing according to the diffusion function corresponding to each pixel in the first image, and the target image is obtained, comprising: Determine a two-dimensional matrix corresponding to the first image; The target image is obtained by performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function.

3. The method according to claim 2, characterized in that Performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain the target image includes: Performing row diffusion processing and column diffusion processing on the two-dimensional matrix according to the diffusion function to obtain a second image; The second image is sharpened to obtain the target image.

4. The method according to claim 3, characterized in that Determining a two-dimensional matrix corresponding to the first image includes: Obtaining a pixel value of each pixel in the first image; Convert the pixel value of each pixel in the first image into a floating point number to obtain a floating point number corresponding to each pixel in the first image; A two-dimensional matrix corresponding to the first image is determined according to floating-point numbers corresponding to each pixel in the first image.

5. The method according to claim 1, characterized in that Determining a noise level of the first image includes: Determine the high pass filter; The first image is processed by the high-pass filter to obtain a noise level of the first image.

6. An image noise reduction processing device, characterized in that: It includes a first determination module, a second determination module, a third determination module, a fourth determination module and a noise reduction module, wherein: The first determination module is used to determine a first image to be subjected to noise reduction processing; The second determination module is used to determine the noise level of the first image and the gradient value of each pixel in the first image; The third determination module is used to determine a gradient threshold according to a noise level of the first image; The fourth determination module is used to determine the diffusion function corresponding to each pixel in the first image according to the gradient value of each pixel in the first image and the gradient threshold; The noise reduction module is used to perform noise reduction processing on the first image according to the diffusion function corresponding to each pixel in the first image to obtain a target image; The fourth determination module is specifically used to determine a first threshold and a second threshold; The diffusion function g(r) corresponding to the pixel is determined according to the following formula 1: in, The r is the modulus of the gradient of the pixel, the k is the gradient threshold, the s is the first threshold, and the h is the second threshold.

7. An image noise reduction processing device, characterized in that: include: Processor, memory; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the image noise reduction processing method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the image denoising method according to any one of claims 1 to 5 when executed by a processor.

9. A computer program product having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electronic device executes the image noise reduction processing method according to any one of claims 1 to 5.

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