Image noise reduction method and device

Abstract

The invention discloses a method and device for image noise reduction, and relates to the field of digital printing technology, so as to solve the problem of poor image quality of printing methods in the prior art. The method comprises: obtaining a target value of the noise point ratio of an image to be printed; calling a target pixel noise reduction matrix corresponding to the target value according to the target value; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library, in which the noise point ratio is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering pixel points in a pixel matrix into clusters and adjusting the distribution and size of the clusters; image noise reduction is performed on the image to be printed based on the target pixel noise reduction matrix; based on this, the pixel noise reduction matrices with different noise point ratios can be called according to demand to perform image noise reduction on the image to be printed, thereby improving the overall quality of the printed image.

Description

Technical Field

[0001] The invention relates to the technical field of digital printing, and in particular to a method and a device for image noise reduction. Background Art

[0002] In the field of digital printing, halftone technology simulates grayscale and color gradient by converting continuous tone images into a series of discrete points. Existing halftone technologies mainly include blue noise halftone technology and traditional error diffusion methods. Among them, blue noise halftone technology reduces visual noise by distributing individual pixels as evenly as possible. This method uses spectral analysis to optimize pixel distribution, making it difficult for the human eye to detect; and blue noise is prone to moiré and detail loss when processing dot enlargement and high-resolution printing; while traditional error diffusion methods propagate quantization errors to adjacent pixels to balance the overall grayscale; such as Floyd-Steinberg diffusion algorithm; traditional error diffusion methods are prone to stripes and block artifacts in high-resolution and high-detail printing, affecting image quality. Therefore, blue noise halftone and traditional error diffusion methods have some limitations in processing dot enlargement, detail fidelity, and anti-distortion capabilities; affecting the printing quality of the image, resulting in poor quality of the printed image.

[0003] In view of this, in order to improve the printing quality of images and meet the printing needs of different users, a new image printing solution is urgently needed to improve the printing quality of images and solve the problem of poor printing quality of images in the printing method in the prior art. Summary of the invention

[0004] The present invention aims to disclose a method and device for reducing image noise, which are used to solve the problem of poor quality of printed images in the printing method in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for image noise reduction, which may include:

[0007] Obtain a target value of the noise point ratio of the image to be printed;

[0008] According to the target value, a target pixel noise reduction matrix corresponding to the target value is called; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library, in which the noise point ratio is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering pixel points in a pixel matrix into clusters and adjusting the distribution and size of the clusters;

[0009] Based on the target pixel noise reduction matrix, image noise reduction is performed on the image to be printed.

[0010] Preferably, the image noise reduction method may further include:

[0011] Determining the distribution and size of the clusters according to the distances between the plurality of pixel points;

[0012] Image noise reduction is performed on the image to be printed according to the distribution and size of the clusters and the characteristics of the printer device.

[0013] Preferably, before calling the target pixel noise reduction matrix corresponding to the target value according to the target value, the method may further include: creating a pixel noise reduction matrix library;

[0014] The created pixel noise reduction matrix library may include:

[0015] Create a first pixel matrix of M×N, where the value corresponding to each pixel point is empty;

[0016] Performing data preprocessing on the first pixel matrix according to a preset pixel value update strategy to obtain a first pixel noise reduction matrix with a noise point ratio of 1 / N;

[0017] Performing data preprocessing on the first pixel noise reduction matrix according to a preset pixel value update strategy to obtain a second pixel noise reduction matrix with a noise point ratio of 2 / N;

[0018] Until the data of the N-1th pixel denoising matrix is ​​preprocessed according to the preset pixel value update strategy, the Nth pixel denoising matrix with a noise point ratio of 1 is obtained.

[0019] Preferably, performing data preprocessing on the first pixel matrix according to a preset pixel value update strategy to obtain a first pixel noise reduction matrix with a noise point ratio of 1 / N may include:

[0020] According to the preset pixel value update strategy, the value corresponding to the first target pixel in the first pixel matrix is ​​updated, and the value of the first target pixel is set to 0 to obtain the first pixel noise reduction matrix; the first target pixel is M pixels with empty values ​​that are screened out from the first pixel matrix according to the preset pixel value update strategy.

[0021] Preferably, performing data preprocessing on the first pixel noise reduction matrix according to a preset pixel value update strategy to obtain a second pixel noise reduction matrix with a noise point ratio of 2 / N may include:

[0022] According to the preset pixel value update strategy, the value corresponding to the second target pixel point in the first pixel denoising matrix is ​​updated, and the value of the second target pixel point is set to 1 to obtain the second pixel denoising matrix; the second target pixel point is M pixel points with empty values ​​that are screened out from the first pixel denoising matrix according to the preset pixel value update strategy.

[0023] Preferably, performing data preprocessing on the N-1th pixel denoising matrix according to a preset pixel value updating strategy to obtain the Nth pixel denoising matrix with a noise point ratio of 1 may include:

[0024] According to the preset pixel value update strategy, the value corresponding to the Nth target pixel in the N-1th pixel denoising matrix is ​​updated, and the value of the Nth target pixel is set to N-1 to obtain the Nth pixel denoising matrix; the Nth target pixel is the M pixel points with empty values ​​that are screened out from the N-1th pixel denoising matrix according to the preset pixel value update strategy.

[0025] Preferably, before performing data preprocessing on the first pixel matrix according to the preset pixel value update strategy, the method includes determining the preset pixel value update strategy;

[0026] The step of determining the preset pixel value update strategy may include:

[0027] Create a second pixel matrix of M×N, where the value corresponding to each pixel point is 0;

[0028] Setting the value of a first intermediate target pixel point to 1, where the first intermediate target pixel point is any pixel point in the second pixel matrix;

[0029] Creating a third pixel matrix of M×N, wherein the value of each pixel point in the third pixel matrix is ​​a random number; the third pixel matrix is ​​a white noise matrix;

[0030] Based on the second pixel matrix and the third pixel matrix, the preset pixel point value update strategy is obtained.

[0031] Preferably, obtaining the preset pixel value update strategy based on the second pixel matrix and the third pixel matrix may include:

[0032] Based on the first intermediate target pixel point, a first preset function model is used to determine a first pixel distance between the first intermediate target pixel point and a first pixel point; the first pixel point is a pixel point with a pixel value of 0 in the second pixel matrix;

[0033] Based on the first pixel distance, a second preset function model is used to update the third pixel matrix to obtain a fourth pixel matrix;

[0034] Performing Gaussian convolution on the second pixel matrix to obtain a fifth pixel matrix;

[0035] Based on the first pixel points, the fourth pixel matrix and the fifth pixel matrix, a sixth pixel matrix is ​​obtained by using a third preset function model;

[0036] Determine a first coordinate corresponding to a pixel point with the largest value in the sixth pixel matrix;

[0037] Based on the first coordinates, determining a second intermediate target pixel point in the second pixel matrix, wherein the coordinates of the second intermediate target pixel point are the same as the first coordinates;

[0038] Setting the value of the second intermediate target pixel to 1;

[0039] Based on the second intermediate target pixel point, a second pixel distance between the second intermediate target pixel point and the first pixel point is determined by using a first preset function model;

[0040] Based on the second pixel distance, using a second preset function model, updating the third pixel matrix to obtain a seventh pixel matrix;

[0041] Performing Gaussian convolution on the second pixel matrix to obtain an eighth pixel matrix;

[0042] Based on the first pixel points, the fourth pixel matrix and the fifth pixel matrix, a ninth pixel matrix is ​​obtained by using a third preset function model;

[0043] Determine the second coordinate corresponding to the pixel point with the largest value in the ninth pixel matrix;

[0044] Based on the second coordinates, determining a third intermediate target pixel point in the second pixel matrix, wherein the coordinates of the third intermediate target pixel point are the same as the second coordinates;

[0045] Setting the value of the third intermediate target pixel to 1;

[0046] Until the value update of the M intermediate target pixel points in the second pixel matrix is ​​completed.

[0047] Preferably, the determining, based on the first intermediate target pixel point, a first preset function model to determine a first pixel distance between the first intermediate target pixel point and the first pixel point may include:

[0048] Using the formula:

[0049] ;

[0050] Determine a first pixel distance between the first intermediate target pixel point and the first pixel point; wherein, is the first pixel distance between the first intermediate target pixel point and the first pixel point, i is the horizontal coordinate corresponding to the first pixel point, j is the vertical coordinate corresponding to the first pixel point, m is the horizontal coordinate corresponding to the first intermediate target pixel point, n is the vertical coordinate corresponding to the first intermediate target pixel point, M is the length of the pixel unit border with the same coordinates as the first pixel point in the third pixel matrix, and N is the width of the pixel unit border with the same coordinates as the first pixel point in the third pixel matrix.

[0051] Preferably, the updating the third pixel matrix based on the first pixel distance and adopting a second preset function model to obtain a fourth pixel matrix may include:

[0052] Determining a matrix update coefficient based on the first pixel distance and in combination with printer device parameters;

[0053] Based on the matrix update coefficients, the third pixel matrix is ​​updated to obtain the fourth pixel matrix.

[0054] Preferably, determining the matrix update coefficient based on the first pixel distance in combination with the printer device parameters may include: using the formula:

[0055] ;

[0056] Get the matrix update coefficients; where is the matrix update coefficient, is the first pixel distance, To print device parameters, is the variance.

[0057] Preferably, updating the third pixel matrix based on the matrix update coefficient to obtain the fourth pixel matrix may include: using the formula:

[0058] ;

[0059] Get a fourth pixel matrix; is the fourth pixel matrix, is the third pixel matrix, is the matrix update coefficient.

[0060] In a second aspect, the present invention provides an image noise reduction device, which may include:

[0061] An acquisition module, the acquisition module is used to acquire a target value of a noise point ratio of an image to be printed;

[0062] A calling module, wherein the calling module is used to call a target pixel noise reduction matrix corresponding to the target value according to the target value; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library, in which the noise point ratio is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering pixel points in a pixel matrix into clusters and adjusting the distribution and size of the clusters;

[0063] A noise reduction module is used to perform image noise reduction on the image to be printed based on the target pixel noise reduction matrix.

[0064] Compared with the prior art, the present invention provides an image denoising method, which obtains a target value of the noise point ratio of an image to be printed; based on the target value, calls a target pixel denoising matrix corresponding to the target value; the target pixel denoising matrix is ​​a pixel denoising matrix in a pre-created pixel denoising matrix library whose noise point ratio is the same as the target value; the pixel denoising matrix library is a pixel denoising matrix library with different noise point ratios obtained by clustering pixel points in the pixel matrix into clusters and adjusting the distribution and size of the clusters; based on the target pixel denoising matrix, image denoising is performed on the image to be printed. Because the image distortion caused by dot enlargement can be reduced after the pixels are clustered, and the pixel groups can be evenly distributed after the clusters are optimized, thereby improving the fidelity and contrast of the image, and improving the grayscale and color of the image and the anti-moiré ability; therefore, the pixel noise reduction matrix with different noise point ratios in the pixel noise reduction matrix library has the characteristics of reducing the image distortion caused by dot enlargement, improving the fidelity and contrast of the image, improving the grayscale and color of the image and the anti-moiré ability; based on this, the image noise reduction method provided by the present invention can call the pixel noise reduction matrix with different noise point ratios in the pixel noise reduction matrix library according to the needs to realize image noise reduction, thereby improving the overall quality of the printed image, and also meeting the different customer needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0066] Figure 1 A schematic diagram of the main process of an image noise reduction method provided by the present invention;

[0067] Figure 2 A schematic diagram of the distribution of values ​​of each pixel point in a third pixel matrix of an image noise reduction method provided by the present invention;

[0068] Figure 3 A schematic diagram of pixel distribution with different noise reduction ratios in a white noise matrix of the prior art;

[0069] Figure 4 A schematic diagram of pixel distribution of different noise reduction ratios of a third pixel matrix of an image noise reduction method provided by the present invention;

[0070] Figure 5a This is a schematic diagram of pixel distribution in the white noise matrix with a noise reduction ratio of 40% in the prior art;

[0071] Figure 5b A schematic diagram of pixel distribution with a noise reduction ratio of 40% in a third pixel matrix of an image noise reduction method provided by the present invention;

[0072] Figure 6 A first schematic diagram of a distribution curve of matrix update coefficients for different printing devices of an image noise reduction method provided by the present invention;

[0073] Figure 7 A second schematic diagram of a distribution curve of matrix update coefficients for different printing devices of an image noise reduction method provided by the present invention;

[0074] Figure 8 A schematic diagram of the distribution of values ​​of each pixel point of a fourth pixel matrix of an image noise reduction method provided by the present invention;

[0075] Fig. 9 A schematic diagram of the distribution of values ​​of each pixel point of a fifth pixel matrix of an image noise reduction method provided by the present invention;

[0076] Fig.10 A schematic diagram of the distribution of values ​​of each pixel point of a sixth pixel matrix of an image noise reduction method provided by the present invention;

[0077] Fig.11 A schematic structural diagram of an image noise reduction device provided by the present invention. DETAILED DESCRIPTION

[0078] In order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second" and the like are used to distinguish the same items or similar items with substantially the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and do not limit their order of precedence. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit them to be different.

[0079] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0080] In the present invention, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects in the previous time are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0081] In the field of digital printing, the commonly used methods of halftone technology include blue noise halftone technology and traditional error diffusion method; among them, the pixel points of blue noise halftone technology are evenly distributed in a single pixel, which is sensitive to dot gain and prone to distortion, resulting in moiré at high resolution and color blocks in color transition; while the pixel distribution of the traditional error diffusion method is that the error propagates between adjacent pixels and is randomly distributed. This distribution method lacks effective control over dot gain, is prone to stripe artifacts, and is prone to block and stripe artifacts, weak anti-distortion ability, and its grayscale transition effect is average, and stripes are prone to appear. Therefore, the technical means in the prior art are insufficient in terms of dot gain, image detail fidelity and anti-distortion ability, resulting in poor quality of printed images.

[0082] In view of this, the present invention provides a method and device for image noise reduction, which adopts a distribution method in which pixels form evenly distributed clusters, and can cluster pixels, thereby reducing image distortion caused by dot enlargement, thereby having higher anti-distortion ability, reducing the generation of moiré, and can smoothly perform grayscale and color transitions, and can reduce color block phenomena; thus improving the overall quality of printed images. Thus, it can solve the problem of poor printed image quality caused by the shortcomings of the prior art in dot enlargement, image detail fidelity and anti-distortion ability.

[0083] Next, the technical solution of the present invention is described in detail with reference to the accompanying drawings:

[0084] See also Figure 1 , Figure 1 A schematic diagram of the main process of an image noise reduction method provided by the present invention; its execution subject is a server or terminal device equipped with the technical solution disclosed in the embodiment of the present invention, such as a printing service platform or a terminal printer device, etc.

[0085] exist Figure 1 In, the method may include:

[0086] Step 110: Obtain a target value of the noise point ratio of the image to be printed.

[0087] Step 120: According to the target value, call the target pixel noise reduction matrix corresponding to the target value; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library, in which the noise point ratio is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering the pixel points in the pixel matrix into clusters and adjusting the distribution and size of the clusters.

[0088] Step 130: performing image noise reduction on the image to be printed based on the target pixel noise reduction matrix.

[0089] In steps 110 to 130, a specific noise point ratio can be set according to user needs, and the noise point ratio is input into the printing system. After the printing system obtains the specific target value of the noise point ratio, it will call the target pixel noise reduction matrix corresponding to the target value from the pre-created pixel noise reduction matrix library according to the target value, and use the target pixel noise reduction matrix to perform image noise reduction on the image to be printed; because the pixel point matrix in the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering the pixel points in the pixel matrix into clusters and adjusting the distribution and size of the clusters, the target pixel noise reduction matrix can be used to reduce image distortion caused by dot enlargement, thereby having higher anti-distortion ability, reducing the generation of moiré, and in smooth grayscale and color transitions, it can reduce color block phenomena, thereby improving the overall quality of the printed image.

[0090] It should be noted that the noise points in the noise point ratio may be pixel points used as printing data, or may be pixel points that need to be filtered out, and may be used flexibly according to actual needs.

[0091] Based on this, by adopting the method disclosed in the present invention, the pixel noise reduction matrix with different noise point proportions in the pixel noise reduction matrix library can be called according to needs to achieve image noise reduction, which can improve the overall quality of the printed image from the aspects of reducing image distortion caused by dot enlargement, improving image fidelity and contrast, improving image grayscale and color, and improving anti-moiré capability.

[0092] Preferably, in step 120, before calling a target pixel noise reduction matrix corresponding to the target value according to the target value, the method may further include: creating a pixel noise reduction matrix library.

[0093] For example, the created pixel noise reduction matrix library may include:

[0094] Create a first pixel matrix of M×N, where the value corresponding to each pixel point is empty.

[0095] The first pixel matrix is ​​subjected to data preprocessing according to a preset pixel value updating strategy to obtain a first pixel noise reduction matrix in which the noise point ratio is 1 / N.

[0096] And, data preprocessing is performed on the first pixel noise reduction matrix according to a preset pixel value update strategy to obtain a second pixel noise reduction matrix with a noise point ratio of 2 / N.

[0097] Until the data of the N-1th pixel denoising matrix is ​​preprocessed according to the preset pixel value update strategy, the Nth pixel denoising matrix with a noise point ratio of 1 is obtained.

[0098] Specifically, performing data preprocessing on the first pixel matrix according to a preset pixel value update strategy to obtain a first pixel noise reduction matrix with a noise point ratio of 1 / N may include:

[0099] According to the preset pixel value update strategy, the value corresponding to the first target pixel in the first pixel matrix is ​​updated, and the value of the first target pixel is set to 0 to obtain the first pixel noise reduction matrix; the first target pixel is M pixels with empty values ​​that are screened out from the first pixel matrix according to the preset pixel value update strategy.

[0100] Wherein, before performing data preprocessing on the first pixel matrix according to the preset pixel value update strategy, it includes determining the preset pixel value update strategy.

[0101] Specifically, the step of determining the preset pixel value update strategy may include:

[0102] First, create a second pixel matrix of M×N, and the value corresponding to each pixel point is 0.

[0103] The value of the first intermediate target pixel point is set to 1, and the first intermediate target pixel point is any pixel point in the second pixel matrix.

[0104] Furthermore, a third pixel matrix of M×N is created, wherein the value of each pixel point in the third pixel matrix is ​​a random number; the third pixel matrix is ​​an optimized white noise matrix; wherein the random number is a random number between [-1, 1].

[0105] In this step, because an M×N third pixel matrix in which the value of each pixel point is a random number [-1, 1] is created, the value range of the pixel point is wider, which is more conducive to improving the image noise reduction effect.

[0106] See also Figures 2 to 5b , Figure 2 A schematic diagram of the distribution of values ​​of each pixel point in a third pixel matrix of an image noise reduction method provided by the present invention; Figure 3 A schematic diagram of pixel distribution with different noise reduction ratios in a white noise matrix of the prior art; Figure 4 A schematic diagram of pixel distribution of different noise reduction ratios of a third pixel matrix of an image noise reduction method provided by the present invention; Figure 5a This is a schematic diagram of pixel distribution in the white noise matrix with a noise reduction ratio of 40% in the prior art; Figure 5b A schematic diagram of pixel distribution with a noise reduction ratio of 40% in a third pixel matrix of an image noise reduction method provided by the present invention.

[0107] exist Figure 2 The value range of each pixel point in the third pixel matrix is ​​a random number between [-1,1], while the value range of each pixel point in the white noise matrix of the prior art is a random number between [0,1]. Experiments show that the image denoising ability of the pixel denoising matrix with a pixel value range of [-1,1] is significantly better than the image denoising ability of the pixel denoising matrix with a pixel value range of [0,1].

[0108] exist Figure 3 to Figure 4 In the schematic diagram of pixel distribution shown; Figure 3b1 in the figure represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 10%, b2 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 20%, b3 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 30%, b4 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 40%, b5 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 50%, b6 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 60%, b7 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 70%, b8 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 80%, and b9 represents a schematic diagram of the distribution of the pixel noise reduction matrix when the noise points account for 90%; Figure 4 a1 in the figure represents the pixel noise reduction matrix distribution diagram when the noise points account for 10%, a2 represents the pixel noise reduction matrix distribution diagram when the noise points account for 20%, a3 represents the pixel noise reduction matrix distribution diagram when the noise points account for 30%, a4 represents the pixel noise reduction matrix distribution diagram when the noise points account for 40%, a5 represents the pixel noise reduction matrix distribution diagram when the noise points account for 50%, a6 represents the pixel noise reduction matrix distribution diagram when the noise points account for 60%, a7 represents the pixel noise reduction matrix distribution diagram when the noise points account for 70%, a8 represents the pixel noise reduction matrix distribution diagram when the noise points account for 80%, and a9 represents the pixel noise reduction matrix distribution diagram when the noise points account for 90%. Figure 4 The pixel distribution ratio Figure 3 The pixels in the image are more densely distributed and appear "hairy". Experiments show that "hairy" pixels are better at filtering out noise. Figure 3 There are "white spots" with large gaps between the pixels in the image. These "white spots" will reduce the noise filtering effect.

[0109] More specifically, Figure 5a The image represents a schematic diagram of pixel noise reduction matrix distribution in the prior art where the noise points in the white noise matrix account for 40% (partially enlarged diagram), Figure 5b The image represents a pixel noise reduction matrix distribution diagram (partial magnification) when the noise points in the third pixel matrix account for 40%; Figure 5a Images and Figure 5b By comparing the images, it is obvious that Figure 5b The pixels in the image are all "hairy", and their area on the entire page is relatively small. Figure 5a Therefore, the third pixel matrix provided by the present invention is used to optimize the pixel values ​​for the first time, which is helpful to filter out image noise and is also more conducive to clustering the pixels.

[0110] Furthermore, the preset pixel point value update strategy can be obtained based on the second pixel matrix and the third pixel matrix.

[0111] The step of obtaining the preset pixel value update strategy based on the second pixel matrix and the third pixel matrix specifically includes:

[0112] Based on the first intermediate target pixel point, the first preset function model is used to determine the first pixel distance between the first intermediate target pixel point and the first pixel point; the first pixel point is a pixel point with a pixel value of 0 in the second pixel matrix; based on the first pixel distance, the second preset function model is used to update the third pixel matrix to obtain a fourth pixel matrix; Gaussian convolution is performed on the second pixel matrix to obtain a fifth pixel matrix; based on the first pixel point, the fourth pixel matrix and the fifth pixel matrix, the third preset function model is used to obtain a sixth pixel matrix; the first coordinate corresponding to the pixel point with the largest value in the sixth pixel matrix is ​​determined; based on the first coordinate, the second intermediate target pixel point in the second pixel matrix is ​​determined, and the coordinate of the second intermediate target pixel point is the same as the first coordinate; the value of the second intermediate target pixel point is set to 1.

[0113] By way of example, determining, based on the first intermediate target pixel point, a first preset function model to determine a first pixel distance between the first intermediate target pixel point and the first pixel point may include:

[0114] Using the formula:

[0115] (1)

[0116] Determine a first pixel distance between the first intermediate target pixel point and the first pixel point; wherein, is the first pixel distance between the first intermediate target pixel point and the first pixel point, i is the horizontal coordinate corresponding to the first pixel point, j is the vertical coordinate corresponding to the first pixel point, m is the horizontal coordinate corresponding to the first intermediate target pixel point, n is the vertical coordinate corresponding to the first intermediate target pixel point, M is the length of the pixel unit border with the same coordinates as the first pixel point in the third pixel matrix, and N is the width of the pixel unit border with the same coordinates as the first pixel point in the third pixel matrix.

[0117] The method of updating the third pixel matrix based on the first pixel distance and adopting the second preset function model to obtain the fourth pixel matrix may include: determining the matrix update coefficient based on the first pixel distance and in combination with the printer device parameters; and updating the third pixel matrix based on the matrix update coefficient to obtain the fourth pixel matrix.

[0118] Specifically, based on the first pixel distance and in combination with the printer device parameters, determining the matrix update coefficient may include using the formula:

[0119] (2)

[0120] Get the matrix update coefficients; where is the matrix update coefficient, is the first pixel distance, To print device parameters, is the variance.

[0121] Updating the third pixel matrix based on the matrix update coefficient to obtain the fourth pixel matrix may include using the formula:

[0122] (3)

[0123] Get a fourth pixel matrix; is the fourth pixel matrix, is the third pixel matrix, is the matrix update coefficient.

[0124] In formula (2), is the printing device parameter, which means that the matrix update coefficient is related to the printing device. For different types of printing devices, adjust The value of , that is, the matrix update coefficient of the corresponding printing device can be obtained, so that the third pixel matrix is ​​updated based on the matrix update coefficient to obtain the fourth pixel matrix.

[0125] As an example, see Figures 6 to 7 , Figure 6 A first schematic diagram of a distribution curve of matrix update coefficients for different printing devices of an image noise reduction method provided by the present invention; Figure 7 A second schematic diagram of a distribution curve of matrix update coefficients for different printing devices of an image noise reduction method provided by the present invention.

[0126] exist Figure 6 In the example, it means that according to the model of the printing device, The matrix update coefficient distribution curve obtained after the value of is adjusted to 10; Figure 6The numbers shown in each of the above numbers represent a pixel point, and the distance between the values ​​represents the pixel distance. The horizontal axis represents the distance in pixels; the vertical axis represents the matrix update coefficient in the probability scaling ratio. It can be seen that the matrix update coefficient distribution curve is normally distributed with a pixel point interval of 10 in the image. For example, the matrix update coefficient values ​​corresponding to the pixels around pixel point 10 are normally distributed symmetrically with pixel point 10 as the center, so that a cluster gathered around pixel point 10 can be obtained; similarly, the matrix update coefficient values ​​corresponding to the pixels around pixel point 20 (30, 40, etc. with a pixel distance of 10) are normally distributed symmetrically with pixel point 20 (30, 40, etc. with a pixel distance of 10) as the center, so that a cluster gathered around pixel point 20 (30, 40, etc. with a pixel distance of 10) can be obtained. The scaling ratio is about 1 pixel distance.

[0127] exist Figure 7 In the example, it means that according to the model of the printing device, The matrix update coefficient distribution curve obtained after the value of is adjusted to 20; Figure 7 The numbers shown in each represent a pixel point, and the distance between the values ​​represents the pixel distance. The horizontal axis represents the distance in pixels; the vertical axis represents the matrix update coefficient in the probability scaling ratio. It can be seen that the matrix update coefficient distribution curve is normally distributed with a pixel interval of 20 in the image. For example, the matrix update coefficient values ​​corresponding to the pixels around the pixel distance of 20 are normally distributed symmetrically with 20 as the center, so that a cluster gathered around 20 can be obtained; similarly, the matrix update coefficient values ​​corresponding to the pixels around the pixel distance of 40 (60, 80, etc., with a pixel distance of 20) are normally distributed symmetrically with 40 (60, 80, etc., with a pixel distance of 20) as the center, so that a cluster gathered around 40 (60, 80, etc., with a pixel distance of 20) can be obtained. The scaling ratio is about 0.5 pixel distance.

[0128] Based on this, The size of the matrix can be adjusted to suit different printing devices to achieve better optimization of the distribution of each pixel in the matrix.

[0129] In practical applications, common printing technologies include laser printing, inkjet printing, and thermal printing. Different printing technologies correspond to different printing devices and applicable scenarios. The printing parameters in different printing device settings will also directly affect the quality of the printed image. For example: printing speed, the printing speed determines how many pages of documents or pictures the printer can output per minute, usually in ppm (pages per minute); printing resolution, resolution is an important parameter that affects the printing quality, and the common resolution unit is dpi (pixels per inch). Therefore, when performing image noise reduction on printed images, it is necessary to combine the factory setting parameters of different printing devices to obtain the printing device parameters for each printing device. Furthermore, the matrix update coefficients for different printing devices can be calculated according to the formula (2) provided by the present invention, and then the matrix update coefficients can be used to update the third pixel matrix to obtain the fourth pixel matrix; since the present invention takes the printing device parameters into consideration in the process of establishing the pixel noise reduction matrix library, the obtained pixel noise reduction matrix library with different noise point proportions is easier to match the corresponding printing device, which is beneficial to improving the noise reduction effect of the image to be printed.

[0130] It should be noted that the printing device parameters It can also be directly informed by the equipment supplier, or calculated by R&D personnel based on historical noise reduction data and factory setting parameters of the printing equipment; this is not specifically limited in this solution.

[0131] For further information, see Figure 8 , Figure 8 A schematic diagram of the distribution of the values ​​of each pixel point of the fourth pixel matrix of an image noise reduction method provided by the present invention; that is, Figure 8 The fourth pixel matrix obtained by updating the third pixel matrix using the matrix update coefficient according to formula (3) is: , achieving the second optimization of the third pixel matrix.

[0132] Preferably, see Figures 9 and 10 , Fig. 9 A schematic diagram of the distribution of values ​​of each pixel point of a fifth pixel matrix of an image noise reduction method provided by the present invention; Fig.10 A schematic diagram of the distribution of values ​​of each pixel point of a sixth pixel matrix of an image denoising method provided by the present invention.

[0133] exist Fig. 9 In this figure, the second pixel matrix is ​​Gaussian convolved to obtain the fifth pixel matrix, using Indicates. Fig.10 In the figure, based on the first pixel point, the fourth pixel matrix and the fifth pixel matrix, the sixth pixel matrix is ​​obtained by using the third preset function model.

[0134] Specifically, the fourth pixel matrix can be based on the first pixel point. Multiply by the fifth pixel matrix The sixth pixel matrix is ​​calculated by the method of , and the pixel point with the largest value in the sixth pixel matrix is ​​further determined, and the coordinates of the pixel point are determined; according to the coordinates of the pixel point, a second intermediate target pixel point with the same coordinates as the pixel point in the second pixel matrix is ​​determined, and the value of the second intermediate target pixel point is set to 1.

[0135] Further, based on the second intermediate target pixel point, the first preset function model is used to determine the second pixel distance between the second intermediate target pixel point and the first pixel point; based on the second pixel distance, the second preset function model is used to update the third pixel matrix to obtain a seventh pixel matrix; Gaussian convolution is performed on the second pixel matrix to obtain an eighth pixel matrix; based on the first pixel point, the fourth pixel matrix and the fifth pixel matrix, the third preset function model is used to obtain a ninth pixel matrix; the second coordinate corresponding to the pixel point with the largest value in the ninth pixel matrix is ​​determined; based on the second coordinate, the third intermediate target pixel point in the second pixel matrix is ​​determined, and the coordinate of the third intermediate target pixel point is the same as the second coordinate; the value of the third intermediate target pixel point is set to 1.

[0136] It should be noted that the method of determining the third intermediate target pixel point based on the second intermediate target pixel point in this step is the same as the method of determining the second intermediate target pixel point based on the first intermediate target pixel point, and will not be repeated here. In this step, based on the first pixel point, the fourth pixel matrix and the fifth pixel matrix, the third preset function model is used to obtain the ninth pixel matrix; the second coordinate corresponding to the pixel point with the largest value in the ninth pixel matrix is ​​determined; the first pixel point is always the pixel point with a corresponding value of 0. In each cycle calculation to obtain the latest ninth pixel matrix, when the pixel point with the largest value in the ninth pixel matrix is ​​screened, the pixel point with a value of 1 is not within the screening range, that is, the pixel point with the latest updated value is screened, and the pixel point whose value has been updated in the previous cycle calculation is not included.

[0137] Until the value update of the M intermediate target pixel points in the second pixel matrix is ​​completed.

[0138] Based on this, the above method can be used to perform the calculation M times in the first cycle to complete the aggregation of M pixels and cluster the M pixels into clusters; that is, the second pixel matrix obtained has the M latest updated intermediate target pixels with a value of 1, and these pixels are all clustered according to the preset pixel value update strategy.

[0139] Finally, the coordinates of M intermediate target pixel points with a value of 1 in the second pixel matrix are determined, and the values ​​of the pixel points corresponding to the M coordinates in the first pixel matrix are set to 0, thereby obtaining the first pixel denoising matrix, which is a first pixel denoising matrix with a noise point ratio of 1 / N; because the coordinates of the pixel points with an updated value of 0 in the first pixel denoising matrix are the same as the coordinates of the M intermediate target pixel points with a value of 1 that are most recently updated in the second pixel matrix, all the M pixel points with a value of 0 in the first pixel denoising matrix are also clustered into clusters according to a preset pixel value update strategy; it has evenly distributed clusters, has higher anti-distortion ability, and reduces the generation of moiré; the printed image quality is high, especially in the case of high resolution and high detail requirements; smooth grayscale and color transitions can reduce the occurrence of color blocks.

[0140] Further, performing data preprocessing on the first pixel noise reduction matrix according to a preset pixel value update strategy to obtain a second pixel noise reduction matrix with a noise point ratio of 2 / N may include:

[0141] According to the preset pixel value update strategy, the value corresponding to the second target pixel point in the first pixel denoising matrix is ​​updated, and the value of the second target pixel point is set to 1 to obtain the second pixel denoising matrix; the second target pixel point is M pixel points with empty values ​​that are screened out from the first pixel denoising matrix according to the preset pixel value update strategy.

[0142] Specifically, the method for determining the second target pixel point is the same as the method for determining the first target pixel point described above, that is, the second cycle calculation can be performed M times according to the above preset pixel value update strategy to complete the aggregation of M pixel points, and the second pixel matrix obtained has the latest updated M intermediate target pixel points with a value of 1, and these pixel points are all clustered according to the preset pixel value update strategy. It should be noted that after the second cycle, there are 2M intermediate target pixel points with a value of 1 in the second pixel matrix, and the coordinate points selected this time are the M pixel points that have been updated to 1 the most recently, that is, the M pixel points obtained after the second cycle calculation M times. Determine the coordinates of the M most recently updated intermediate target pixel points with a value of 1 in the second pixel matrix, and set the values ​​of the pixel points corresponding to the M coordinates in the first pixel matrix to 1, thereby obtaining the second pixel denoising matrix, a first pixel denoising matrix in which the noise point ratio is 2 / N; because the coordinates of the pixel points with an updated value of 1 in the first pixel denoising matrix are the same as the coordinates of the M most recently updated intermediate target pixel points with a value of 1 in the second pixel matrix, all the M pixel points with a value of 1 in the second pixel denoising matrix are also pixel points clustered according to a preset pixel value update strategy.

[0143] Furthermore, in order to establish a complete pixel denoising matrix library, the preset pixel value update strategy can be used to perform a loop calculation N times to complete the update of all empty pixel values ​​in the established M×N first pixel matrix. Specifically, the N-1 pixel denoising matrix can be preprocessed according to the preset pixel value update strategy to obtain the N-1 pixel denoising matrix with a noise point ratio of 1, including:

[0144] According to the preset pixel value update strategy, the value corresponding to the Nth target pixel in the N-1th pixel denoising matrix is ​​updated, and the value of the Nth target pixel is set to N-1 to obtain the Nth pixel denoising matrix; the Nth target pixel is the M pixel points with empty values ​​that are screened out from the N-1th pixel denoising matrix according to the preset pixel value update strategy.

[0145] Specifically, the method for determining the Nth pixel noise reduction matrix is ​​the same as the method for determining the first pixel noise reduction matrix and the second pixel noise reduction matrix, which will not be described in detail herein.

[0146] Based on this, the value range of each pixel point in the pixel noise reduction matrix library finally obtained according to the above method is 0 to N-1, that is, M pixel points with a value of 0 are clustered, M pixel points with a value of 1 are clustered, M pixel points with a value of 2 are clustered... and M pixel points with a value of N-1 are clustered. This makes it possible to achieve image noise reduction for the printed image according to different proportions of noise points.

[0147] In practical applications, the image noise reduction method provided by the present invention not only improves the overall quality of printed images, but also performs well in processing complex grayscale and color transitions, providing reliable technical support for high-quality printing and image processing.

[0148] As a more specific embodiment, a first pixel matrix with a size of 256×256 is established, and according to the preset pixel point value update strategy provided by the present invention, 256 cycles are used as an update unit and updated 256 times.

[0149] After the first update, the 256 points with empty values ​​in the first pixel matrix are set to 0.

[0150] After the second update, the 256 points with empty values ​​in the first pixel matrix are set to 1.

[0151] After the third update, the 256 points with empty values ​​in the first pixel matrix are set to 2.

[0152] After the fourth update, the 256 points with empty values ​​in the first pixel matrix are set to 3. .........

[0154] After the i-th update, the 256 points with empty values ​​in the first pixel matrix are set to i-1. .........

[0156] After the 255th update, the 256 points in the first pixel matrix whose values ​​are empty are set to 254.

[0157] After the 256th update, the 256 points in the first pixel matrix whose values ​​are empty are set to 255.

[0158] Based on this, we can obtain a pixel denoising matrix library with pixel values ​​corresponding to 0-255.

[0159] According to user needs, if an image with a noise point ratio of 1 / 256 is required, the pixel noise reduction matrix corresponding to the point with a maximum pixel value of 0 can be called in the pixel noise reduction matrix library.

[0160] If an image with a noise point ratio of 50% is required, the pixel noise reduction matrix corresponding to the points with a maximum pixel value less than 128 can be called in the pixel noise reduction matrix library; thereby, the pixel noise reduction matrix with different noise point ratios can be called according to needs to perform image noise reduction on the printed image, thereby improving the overall quality of the printed image.

[0161] In a second aspect, based on the same idea, the present invention provides an image noise reduction device, which may include:

[0162] The acquisition module 1110 is used to acquire a target value of the noise point ratio of the image to be printed.

[0163] The calling module 1120 is used to call the target pixel noise reduction matrix corresponding to the target value according to the target value; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library, in which the noise point ratio is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering the pixel points in the pixel matrix into clusters and adjusting the distribution and size of the clusters.

[0164] The noise reduction module 1130 is used to perform image noise reduction on the image to be printed based on the target pixel noise reduction matrix.

[0165] The present invention provides an image noise reduction device, which obtains a target value of the noise point ratio of an image to be printed through an acquisition module; then uses a calling module to call a target pixel noise reduction matrix corresponding to the target value; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library with a noise point ratio that is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering pixel points in a pixel matrix and adjusting the distribution and size of the clusters; finally, a noise reduction module 1130 is used to perform image noise reduction on the image to be printed based on the target pixel noise reduction matrix; because the pixel noise reduction matrices with different noise point ratios in the pixel noise reduction matrix library are pixel points that are clustered and cluster-optimized, they have the characteristics of reducing image distortion caused by dot enlargement, improving image fidelity and contrast, improving image grayscale and color, and improving anti-moiré capability; therefore, the image noise reduction device provided by the present invention can realize image noise reduction by calling the pixel noise reduction matrices with different noise point ratios in the pixel noise reduction matrix library according to needs, thereby improving the overall quality of the printed image.

[0166] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0167] Although the present invention has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present invention. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present invention as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present invention. Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such modifications and variations if they fall within the scope of the claims of the present invention and their equivalents.

Claims

1. A method for image denoising, characterized in that: include: Obtain the target value of the noise point ratio of the image to be printed and the created pixel noise reduction matrix library; The created pixel noise reduction matrix library includes: creating an M×N first pixel matrix, in which the value corresponding to each pixel point is empty; performing data preprocessing on the first pixel matrix according to a preset pixel point value update strategy to obtain a first pixel noise reduction matrix with a noise point ratio of 1 / N; performing data preprocessing on the first pixel noise reduction matrix according to a preset pixel point value update strategy to obtain a second pixel noise reduction matrix with a noise point ratio of 2 / N; until performing data preprocessing on the N-1th pixel noise reduction matrix according to the preset pixel point value update strategy to obtain the Nth pixel noise reduction matrix with a noise point ratio of 1; The method of performing data preprocessing on the first pixel matrix according to a preset pixel value update strategy to obtain a first pixel noise reduction matrix with a noise point ratio of 1 / N includes: updating data corresponding to a first target pixel point in the first pixel matrix according to the preset pixel value update strategy, setting the first target pixel point value to 0, and obtaining the first pixel noise reduction matrix; the first target pixel point is M pixel points with empty values ​​screened out from the first pixel matrix according to the preset pixel value update strategy; The method of performing data preprocessing on the first pixel denoising matrix according to a preset pixel value updating strategy to obtain a second pixel denoising matrix with a noise point ratio of 2 / N includes: updating data corresponding to a second target pixel point in the first pixel denoising matrix according to the preset pixel value updating strategy, setting the second target pixel point value to 1, and obtaining the second pixel denoising matrix; the second target pixel point is M pixel points with empty values ​​screened out from the first pixel denoising matrix according to the preset pixel value updating strategy; According to the target value, a target pixel noise reduction matrix corresponding to the target value is called; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library, in which the noise point ratio is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering pixel points in a pixel matrix into clusters and adjusting the distribution and size of the clusters; Based on the target pixel noise reduction matrix, image noise reduction is performed on the image to be printed.

2. The method for image noise reduction according to claim 1, characterized in that: Also includes: Determining the distribution and size of the clusters according to the distances between the plurality of pixel points; Image noise reduction is performed on the image to be printed according to the distribution and size of the clusters and the characteristics of the printer device.

3. The method for image noise reduction according to claim 1, characterized in that: The method of performing data preprocessing on the N-1th pixel noise reduction matrix according to a preset pixel value update strategy to obtain the Nth pixel noise reduction matrix with a noise point ratio of 1 includes: According to the preset pixel value update strategy, the value corresponding to the Nth target pixel in the N-1th pixel denoising matrix is ​​updated, and the value of the Nth target pixel is set to N-1 to obtain the Nth pixel denoising matrix; the Nth target pixel is the M pixel points with empty values ​​that are screened out from the N-1th pixel denoising matrix according to the preset pixel value update strategy.

4. The method for image noise reduction according to claim 1, characterized in that: Before performing data preprocessing on the first pixel matrix according to the preset pixel value update strategy, the method includes determining the preset pixel value update strategy; The step of determining the preset pixel value update strategy includes: Create a second pixel matrix of M×N, where the value corresponding to each pixel is 0; Setting the value of a first intermediate target pixel point to 1, where the first intermediate target pixel point is any pixel point in the second pixel matrix; Creating a third pixel matrix of M×N, wherein the value of each pixel point in the third pixel matrix is ​​a random number; the third pixel matrix is ​​a white noise matrix; Based on the second pixel matrix and the third pixel matrix, the preset pixel point value update strategy is obtained.

5. The method for image noise reduction according to claim 4, characterized in that: The step of obtaining the preset pixel value update strategy based on the second pixel matrix and the third pixel matrix includes: Based on the first intermediate target pixel point, a first preset function model is used to determine a first pixel distance between the first intermediate target pixel point and a first pixel point; the first pixel point is a pixel point with a pixel value of 0 in the second pixel matrix; Based on the first pixel distance, a second preset function model is used to update the third pixel matrix to obtain a fourth pixel matrix; Performing Gaussian convolution on the second pixel matrix to obtain a fifth pixel matrix; Based on the first pixel points, the fourth pixel matrix and the fifth pixel matrix, a sixth pixel matrix is ​​obtained by using a third preset function model; Determine a first coordinate corresponding to a pixel point with the largest value in the sixth pixel matrix; Based on the first coordinates, determining a second intermediate target pixel point in the second pixel matrix, wherein the coordinates of the second intermediate target pixel point are the same as the first coordinates; Setting the value of the second intermediate target pixel to 1; Based on the second intermediate target pixel point, a second pixel distance between the second intermediate target pixel point and the first pixel point is determined by using a first preset function model; Based on the second pixel distance, using a second preset function model, updating the third pixel matrix to obtain a seventh pixel matrix; Performing Gaussian convolution on the second pixel matrix to obtain an eighth pixel matrix; Based on the first pixel points, the fourth pixel matrix and the fifth pixel matrix, a ninth pixel matrix is ​​obtained by using a third preset function model; Determine the second coordinate corresponding to the pixel point with the largest value in the ninth pixel matrix; Based on the second coordinates, determining a third intermediate target pixel point in the second pixel matrix, wherein the coordinates of the third intermediate target pixel point are the same as the second coordinates; Setting the value of the third intermediate target pixel to 1; Until the value update of the M intermediate target pixel points in the second pixel matrix is ​​completed.

6. The method for image noise reduction according to claim 5, characterized in that: The determining, based on the first intermediate target pixel point, a first preset function model, a first pixel distance between the first intermediate target pixel point and the first pixel point includes: Using the formula: ； Determine a first pixel distance between the first intermediate target pixel point and the first pixel point; wherein, is the first pixel distance between the first intermediate target pixel point and the first pixel point, i is the horizontal coordinate corresponding to the first pixel point, j is the ordinate corresponding to the first pixel point, m is the horizontal coordinate corresponding to the first intermediate target pixel point, n is the ordinate corresponding to the first intermediate target pixel point, M is the length of the border of the pixel unit in the third pixel matrix with the same coordinates as the first pixel point, N is the width of the border of the pixel unit in the third pixel matrix having the same coordinates as the first pixel point.

7. The method for image noise reduction according to claim 5, characterized in that: The method of updating the third pixel matrix based on the first pixel distance and adopting a second preset function model to obtain a fourth pixel matrix comprises: Based on the first pixel distance and in combination with the printer device parameters, a matrix update coefficient is determined; the matrix update coefficient is a matrix update coefficient obtained after data calculation according to a second preset function model based on the first pixel distance and the printer device parameters; Based on the matrix update coefficients, the third pixel matrix is ​​updated to obtain the fourth pixel matrix.

8. The method for image noise reduction according to claim 7, characterized in that: The determining of the matrix update coefficient based on the first pixel distance and in combination with the printer device parameters includes: Using the formula: ； Get the matrix update coefficients; where is the matrix update coefficient, is the first pixel distance, To print device parameters, is the variance; The updating of the third pixel matrix based on the matrix update coefficient to obtain the fourth pixel matrix includes: Using the formula: ； Get a fourth pixel matrix; is the fourth pixel matrix, is the third pixel matrix, is the matrix update coefficient.

9. A device for image noise reduction, characterized in that: include: An acquisition module, the acquisition module is used to acquire a target value of a noise point ratio of an image to be printed and a created pixel noise reduction matrix library; The created pixel noise reduction matrix library includes: creating an M×N first pixel matrix, in which the value corresponding to each pixel point is empty; performing data preprocessing on the first pixel matrix according to a preset pixel point value update strategy to obtain a first pixel noise reduction matrix with a noise point ratio of 1 / N; performing data preprocessing on the first pixel noise reduction matrix according to a preset pixel point value update strategy to obtain a second pixel noise reduction matrix with a noise point ratio of 2 / N; until performing data preprocessing on the N-1th pixel noise reduction matrix according to the preset pixel point value update strategy to obtain the Nth pixel noise reduction matrix with a noise point ratio of 1; The method of performing data preprocessing on the first pixel matrix according to a preset pixel value update strategy to obtain a first pixel noise reduction matrix with a noise point ratio of 1 / N includes: updating data corresponding to a first target pixel point in the first pixel matrix according to the preset pixel value update strategy, setting the first target pixel point value to 0, and obtaining the first pixel noise reduction matrix; the first target pixel point is M pixel points with empty values ​​screened out from the first pixel matrix according to the preset pixel value update strategy; The method of performing data preprocessing on the first pixel denoising matrix according to a preset pixel value updating strategy to obtain a second pixel denoising matrix with a noise point ratio of 2 / N includes: updating data corresponding to a second target pixel point in the first pixel denoising matrix according to the preset pixel value updating strategy, setting the second target pixel point value to 1, and obtaining the second pixel denoising matrix; the second target pixel point is M pixel points with empty values ​​screened out from the first pixel denoising matrix according to the preset pixel value updating strategy; A calling module, wherein the calling module is used to call a target pixel noise reduction matrix corresponding to the target value according to the target value; the target pixel noise reduction matrix is ​​a pixel noise reduction matrix in a pre-created pixel noise reduction matrix library, in which the noise point ratio is the same as the target value; the pixel noise reduction matrix library is a pixel noise reduction matrix library with different noise point ratios obtained by clustering pixel points in a pixel matrix into clusters and adjusting the distribution and size of the clusters; A noise reduction module is used to perform image noise reduction on the image to be printed based on the target pixel noise reduction matrix.

Images