Image denoising method based on adaptive value range filtering, electronic device, medium

Abstract

The application discloses an image denoising method based on adaptive value range filtering, an electronic device and a medium, and relates to the technical field of image processing.The method comprises the following steps: acquiring an image to be processed, setting a segmentation threshold, and segmenting the image to be processed into a defect region and a background region according to the segmentation threshold; filtering and morphologically processing the defect region; setting corresponding kernel standard deviation parameters for the defect region and the background region; and performing adaptive value range filtering on each pixel point in the defect region and the background region based on the difference between the neighborhood pixel points and the neighborhood mean value of the pixel point, and performing neighborhood weighted summation on the pixel point based on the weight to obtain a denoised image.The application realizes denoising processing of the image background and the defect region, removes background noise, and retains gradient information of the defect region as much as possible.

Description

Technical Field

[0001] This invention relates to the field of image processing, and more particularly to an image denoising method, electronic device, and medium based on adaptive range filtering. Background Technology

[0002] When noise is significant in an image, the feature information of defective regions is interfered with, making it difficult to distinguish between defective and background regions and reducing image quality. While commonly used methods like mean filtering can effectively remove noise from the background, they also lose gradient and edge information from defective regions. Methods based on neighboring pixel similarity, such as bilateral filtering, result in poor denoising because the weighted contribution of neighboring similar pixels is too large for significant noise points. In actual industrial production, high efficiency is required for image processing, thus denoising methods involving extensive and complex calculations cannot be used. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing an image denoising method, electronic device, and medium based on adaptive range filtering.

[0004] In a first aspect, embodiments of the present invention provide an image denoising method based on adaptive range filtering, the method comprising:

[0005] Acquire the image to be processed, set a segmentation threshold, and segment the image to be processed into defect regions and background regions according to the segmentation threshold;

[0006] Filter and perform morphological processing on defective areas;

[0007] The kernel standard deviation parameters are set for the defect area and the background area respectively; and the neighbor weighted summation is performed on each pixel in the defect area and the background area based on the difference between its neighboring pixels and the mean of the neighborhood to achieve adaptive value range filtering, so as to obtain the denoised image.

[0008] Furthermore, the process of setting the segmentation threshold includes:

[0009] Plot the grayscale distribution curve of the image to be processed, and find the region in the grayscale distribution curve that is approximately a straight line;

[0010] Select two points from the region that approximates a straight line, and denote them as points A and B;

[0011] Draw a fitted straight line passing through points A and B;

[0012] As the pixel value increases, the difference between each pixel value in the grayscale distribution curve and the fitted straight line value is calculated. When the difference is less than the preset difference threshold, the current pixel value is used as the segmentation threshold.

[0013] Furthermore, plotting the grayscale distribution curve of the image to be processed includes:

[0014] Plot the grayscale distribution curve of the image to be processed with the number of pixels in the image as the x-axis and the pixel value of the image as the y-axis.

[0015] Furthermore, filtering defective areas includes:

[0016] Connectivity analysis was performed on the segmented defect regions to obtain several connected regions;

[0017] For each connected region, set a corresponding threshold for the area, width, height, or gradient distribution to perform filtering.

[0018] Furthermore, morphological processing of the defective area includes:

[0019] The defective area is expanded to include the defective edge, and voids in the defective area are eliminated.

[0020] Furthermore, corresponding kernel standard deviation parameters are set for the defect region and the background region; and for each pixel in the defect region and the background region, a neighborhood weighted sum is performed based on the difference between its neighboring pixels and the mean of the neighborhood, and the expression for adaptive range filtering is as follows:

[0021]

[0022] In the formula, I(i+m,j+n) represents the neighboring pixel values ​​of pixel I(i,j), and I mean (i,j) represents the pixel mean of the local region where pixel I(i,k) is located, and σ represents the kernel standard deviation parameter.

[0023] Furthermore, based on the degree of gradient information retention and denoising in the background and defect regions, corresponding kernel standard deviation parameters are set for the defect and background regions; the larger the kernel standard deviation parameter value, the smaller the degree of gradient information retention and the stronger the denoising.

[0024] Secondly, embodiments of the present invention provide an image denoising system based on adaptive range filtering, used to implement the above-mentioned image denoising method based on adaptive range filtering, the system comprising:

[0025] The image segmentation module is used to acquire the image to be processed, set the segmentation threshold, and segment the image to be processed into defect areas and background areas according to the segmentation threshold.

[0026] The defect area processing module is used for filtering and morphological processing of defect areas;

[0027] The filtering and denoising module is used to set corresponding kernel standard deviation parameters for defect areas and background areas; and to perform adaptive range filtering by weighting the neighboring pixels and summing the values ​​of each pixel in the defect area and background area based on the difference between the neighboring pixels and the mean of the neighboring area, so as to obtain the denoised image.

[0028] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above-described image denoising method based on adaptive range filtering.

[0029] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the above-described image denoising method based on adaptive range filtering.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention targets noise in images, and based on the different requirements of the background region and the defect region for the degree of gradient information preservation and denoising, draws gray-level distribution curves and calculates segmentation thresholds to segment the defect and background regions. Based on the adaptive weights of local regions and different filtering parameters, adaptive range filtering is performed on the defect region and the background region respectively, which can better preserve the defect details while achieving denoising effect. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of an image denoising method based on adaptive range filtering provided in an embodiment of the present invention;

[0033] Figure 2 The image to be denoised is provided in the embodiments of the present invention;

[0034] Figure 3 This is a schematic diagram of the pixel distribution curve and the fitted straight line effect provided in an embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram of the segmented defect region provided in an embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of the denoising results, original image, and result image of the denoising results of defects and background areas provided in the embodiments of the present invention;

[0037] Figure 6 This is a schematic diagram of an image denoising system based on adaptive range filtering provided in an embodiment of the present invention.

[0038] Figure 7 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0040] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0041] It should be understood that although the terms first, second, third, etc., may be used in this invention to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of this invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0042] The present invention will now be described in detail with reference to the accompanying drawings. Unless otherwise specified, the features of the following embodiments and implementations can be combined with each other.

[0043] like Figure 1 As shown, this embodiment of the invention provides an image denoising method based on adaptive range filtering, the method specifically including the following steps:

[0044] Step S1: Obtain the image to be processed, set the segmentation threshold, and segment the image to be processed into defect areas and background areas according to the segmentation threshold.

[0045] Furthermore, Figure 2 An image to be processed according to an embodiment of the present invention is shown. The image to be processed is a 16-bit single-channel image, and in this example, the image to be processed will also be normalized.

[0046] Furthermore, the process of setting the segmentation threshold includes: sorting the pixel values ​​of the image to be processed and drawing a grayscale distribution curve; finding a region in the grayscale distribution curve that approximates a straight line; selecting two points from this region, denoted as points A and B; drawing a fitted straight line passing through points A and B; and as the pixel values ​​increase, calculating the difference between each pixel value in the grayscale distribution curve and the fitted straight line value. When the difference is less than a preset difference threshold, the current pixel value is used as the segmentation threshold.

[0047] Specifically, such as Figure 3 As shown, a grayscale distribution curve of the image to be processed is plotted with the number of pixels on the x-axis and the pixel values ​​on the y-axis. A certain range of pixel values ​​within an approximately straight line region in the middle of the grayscale distribution curve is used for linear fitting. In this example, the range between lines A and B is selected, and a fitted straight line passing through points A and B is plotted. Based on the pixel range of the grayscale distribution curve, along... Figure 3 The x-axis is used to calculate the difference between each pixel value in the grayscale distribution curve and the fitted straight line value, from smallest to largest. The pixel value corresponding to the first difference that is less than a preset threshold is used as the segmentation threshold. Point C is an example of the selected segmentation threshold. Based on the segmentation threshold, the image to be processed is initially segmented into defect regions and background regions.

[0048] Step S2: Filter the defective regions obtained from the segmentation in step S1.

[0049] Specifically, in this example, connectivity analysis is performed on the segmented defect regions to obtain several connected regions; each connected region is considered a defect. Based on the general characteristics of defects, corresponding thresholds are set for the area, width, height, or gradient distribution of each connected region for filtering.

[0050] Figure 4 The diagram shows the segmented defect area before and after filtering. Figure 4 Figure (a) in the image is a diagram of the defect area before filtering. Figure 4 (b) in the figure is the filtered defect area map, which is composed of (a) and Figure 4 As can be seen from (b) in the figure, filtering the defective region makes the edges of each connected region in the defective region clearer.

[0051] Step S3: Perform morphological processing on the defective regions filtered in step S2.

[0052] Specifically, in this example, morphological processing such as dilation is performed on the defect area to reduce segmentation error, so that the segmented defect area contains the defect edge as much as possible, and holes caused by noise in the internal area of ​​the defect are eliminated as much as possible.

[0053] Step S4: Set corresponding kernel standard deviation parameters for the defect area and the background area, and perform adaptive range filtering by weighting the neighboring pixels and summing the values ​​of each pixel in the defect area and the background area based on the difference between the neighboring pixels and the mean of the neighboring area, so as to obtain the denoised image.

[0054] Furthermore, corresponding kernel standard deviation parameters are set for the defect region and the background region. For each pixel in the defect region and the background region, a neighborhood weighted sum is performed based on the difference between its neighboring pixels and the mean of the neighborhood to achieve adaptive range filtering. The expression is as follows:

[0055]

[0056] In the formula, I(i+m,k+n) represents the neighboring pixel values ​​of pixel I(i,j), and I mean (i,j) represents the pixel mean of the local region where pixel I(i,j) is located, and σ represents the kernel standard deviation parameter.

[0057] It should be noted that the smaller the difference between a pixel's neighboring pixels and the mean of its neighborhood, the greater its weight.

[0058] In this example, based on the degree of gradient information preservation and denoising in the background and defect regions, corresponding kernel standard deviation parameters σ are set for the defect and background regions. The larger the kernel standard deviation parameter σ, the smaller the degree of gradient information preservation and the stronger the denoising effect.

[0059] See Figure 5 As shown, Figure 5 (c) in the figure is the denoising result obtained by the embodiment of the present invention. Figure 5 (a) and Figure 5 (d) in the image represent magnified views of the corresponding regions in the original image. Figure 5 (b) and Figure 5 In the image, (e) represents a magnified view of the selected defect and background areas in the denoised result image. Figure 5 It can be seen that the noise fluctuations in the defect area and background area of ​​the image processed by the method of the present invention are greatly reduced, and the gradient features of the defect area are well preserved and highlighted.

[0060] In summary, the method of the present invention is based on the different requirements of the background region and the defect region for the degree of gradient information preservation and denoising. It calculates the segmentation threshold according to the overall pixel distribution of the image to segment the defect and background regions, and performs adaptive range filtering on the defect region and the background region respectively based on the adaptive weight of the local region and different filtering parameters. This achieves denoising effect while preserving the defect details well.

[0061] like Figure 6As shown, this embodiment of the invention also provides an image denoising system based on adaptive range filtering, the system comprising:

[0062] The image segmentation module is used to acquire the image to be processed, set the segmentation threshold, and segment the image to be processed into defect areas and background areas according to the segmentation threshold.

[0063] The defect area processing module is used for filtering and morphological processing of defect areas;

[0064] The filtering and denoising module is used to set corresponding kernel standard deviation parameters for defect areas and background areas; and to perform adaptive range filtering by weighting the neighboring pixels and summing the values ​​of each pixel in the defect area and background area based on the difference between the neighboring pixels and the mean of the neighboring area, so as to obtain the denoised image.

[0065] This specification also provides a computer-readable storage medium storing a computer program that can be used to perform the above-described data synchronization method.

[0066] This instruction manual also provides Figure 7 The diagram shows a schematic structural representation of the electronic device. Figure 7 At the hardware level, the electronic device includes a processor, an internal bus, a network interface, memory, and non-volatile storage, and may also include other hardware required for the business operations. The processor reads the corresponding computer program from the non-volatile storage into memory and then runs it to achieve the aforementioned data synchronization method.

[0067] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0068] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0069] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0070] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0071] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0072] Those skilled in the art will understand that embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0078] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0079] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0080] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0082] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0083] The above description is merely an embodiment of this specification and is not intended to limit this specification. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

[0084] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An image denoising method based on adaptive range filtering, characterized in that, The method includes: Acquire the image to be processed, set a segmentation threshold, and segment the image to be processed into defect regions and background regions according to the segmentation threshold; Filter and perform morphological processing on defective areas; The kernel standard deviation parameters are set for the defect region and the background region respectively; and for each pixel in the defect region and the background region, a weighted summation is performed based on the difference between its neighboring pixels and the mean of the neighboring pixels to achieve adaptive range filtering, resulting in the denoised image; the expression is as follows: ； In the formula, For pixels The neighboring pixel values, Represents pixels The average pixel value of the local region. This represents the kernel standard deviation parameter.

2. The image denoising method based on adaptive range filtering according to claim 1, characterized in that, The process of setting the segmentation threshold includes: Plot the grayscale distribution curve of the image to be processed, and find the region in the grayscale distribution curve that is approximately a straight line; Select two points from the region that approximates a straight line, and denote them as points A and B; Draw a fitted straight line passing through points A and B; As the pixel value increases, the difference between each pixel value in the grayscale distribution curve and the fitted straight line value is calculated. When the difference is less than the preset difference threshold, the current pixel value is used as the segmentation threshold.

3. The image denoising method based on adaptive range filtering according to claim 2, characterized in that, Plotting the grayscale distribution curve of the image to be processed includes: Plot the grayscale distribution curve of the image to be processed with the number of pixels on the x-axis and the pixel value on the y-axis.

4. The image denoising method based on adaptive range filtering according to claim 1, characterized in that, Filtering defective areas includes: Connectivity analysis was performed on the segmented defect regions to obtain several connected regions; For each connected region, set a corresponding threshold for the area, width, height, or gradient distribution to perform filtering.

5. The image denoising method based on adaptive range filtering according to claim 1, characterized in that, Morphological processing of defective areas includes: The defective area is expanded to include the defective edge and voids in the defective area are eliminated.

6. The image denoising method based on adaptive range filtering according to claim 1, characterized in that, Based on the degree of gradient information preservation and noise reduction in the background and defect regions, corresponding kernel standard deviation parameters are set for the defect and background regions. The larger the kernel standard deviation parameter value, the less gradient information is preserved and the stronger the denoising effect.

7. An image denoising system based on adaptive range filtering, characterized in that, The system for implementing the image denoising method based on adaptive range filtering according to any one of claims 1-6, the system comprising: The image segmentation module is used to acquire the image to be processed, set the segmentation threshold, and segment the image to be processed into defect areas and background areas according to the segmentation threshold. The defect area processing module is used for filtering and morphological processing of defect areas. The filtering and denoising module is used to set corresponding kernel standard deviation parameters for defect areas and background areas; and to perform adaptive range filtering by weighting the neighboring pixels and summing the values ​​of each pixel in the defect area and background area based on the difference between the neighboring pixels and the mean of the neighboring area, so as to obtain the denoised image.

8. An electronic device comprising a memory and a processor, characterized in that, The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the image denoising method based on adaptive range filtering as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the image denoising method based on adaptive range filtering as described in any one of claims 1-6.

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