Image denoising method, electronic equipment and computer readable medium

Abstract

The invention provides an image denoising method, electronic equipment and a computer readable medium. The image denoising method comprises the following steps: determining an adaptive smoothing parameter corresponding to a current to-be-updated pixel point according to a local adjustment gain and an initial smoothing parameter corresponding to the current to-be-updated pixel point in a target image; wherein the local adjustment gains corresponding to the to-be-updated pixel points meeting different conditions are different; updating the Y channel pixel value of the current to-be-updated pixel point according to the adaptive smoothing parameter corresponding to the current to-be-updated pixel point to obtain an updated Y channel pixel value of the current to-be-updated pixel point; and setting another to-be-updated pixel point in the target image as the current to-be-updated pixel point, and continuing to repeat the steps until the Y channel pixel values of all to-be-updated pixel points in the target image are updated.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of image processing technology, and in particular to an image denoising method, an electronic device, and a computer-readable medium. Background Art

[0002] Most of the spatial domain denoising techniques use relatively simple algorithms such as mean filtering, median filtering, and Gaussian filtering. These algorithms all use a fixed window to filter the image, which is called a local filtering algorithm. The classic non-local means (NLM) filtering algorithm measures the similarity between the sliding window and the specified window by searching in the image block and calculating the Euclidean distance between them, thereby determining the weighted average weight for filtering. It is a non-local filtering algorithm. The NLM filtering algorithm takes into account the self-similarity of the image and makes full use of the redundant information in the image. It can maintain the detailed features of the image to the greatest extent while denoising, but the filtering effect is still not good. Summary of the invention

[0003] Embodiments of the present application provide an image denoising method, an electronic device, and a computer-readable medium.

[0004] In a first aspect, an embodiment of the present application provides an image denoising method, comprising: determining an adaptive smoothing parameter corresponding to a current pixel to be updated in a target image based on a local adjustment gain and an initial smoothing parameter corresponding to the current pixel to be updated; wherein the local adjustment gains corresponding to pixels to be updated that satisfy different conditions are different; updating the Y channel pixel value of the current pixel to be updated based on the adaptive smoothing parameter corresponding to the current pixel to be updated to obtain an updated Y channel pixel value of the current pixel to be updated; setting another pixel to be updated in the target image as the current pixel to be updated, and continuing to repeat the above steps until the Y channel pixel values ​​of all pixels to be updated in the target image are updated.

[0005] In a second aspect, an embodiment of the present application provides an image denoising device, comprising: an adaptive smoothing parameter determination module, used to determine the adaptive smoothing parameter corresponding to the pixel to be updated in the target image according to the local adjustment gain and initial smoothing parameter corresponding to the pixel to be updated in the target image; wherein the local adjustment gain corresponding to the pixel to be updated that satisfies different conditions is different; an updating module, used to update the Y channel pixel value of the pixel to be updated according to the adaptive smoothing parameter corresponding to the pixel to be updated to obtain the updated Y channel pixel value of the pixel to be updated; the adaptive smoothing parameter determination module is also used to: set another pixel to be updated in the target image as the current pixel to be updated, and continue to repeat the above steps until the Y channel pixel values ​​of all pixels to be updated in the target image are updated.

[0006] In a third aspect, an embodiment of the present application provides an electronic device, comprising: at least one processor; a memory, wherein at least one program is stored in the memory, and when the at least one program is executed by the at least one processor, any one of the above-mentioned image denoising methods is implemented.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable medium having a computer program stored thereon, and when the computer program is executed by a processor, any one of the above-mentioned image denoising methods is implemented.

[0008] The image denoising method provided in the embodiment of the present application obtains an adaptive smoothing parameter based on a local adjustment gain. Since the local adjustment gains corresponding to the pixels to be updated that meet different conditions are different, the adaptive smoothing parameters corresponding to the pixels to be updated that meet different conditions are also different. As a result, the pixels to be updated that meet different conditions are denoised to different degrees, thereby greatly improving the denoising effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of an image denoising method provided by an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the positional relationship and size relationship of the central neighborhood box, the search box, and the matching neighborhood box in an embodiment of the present application;

[0011] Figure 3 A block diagram of a device for image denoising provided by another embodiment of the present application;

[0012] Figure 4 A block diagram of the composition of an electronic device provided in accordance with another embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the technical solution of the present application, the image denoising method, electronic device, and computer-readable medium provided by the present application are described in detail below in conjunction with the accompanying drawings.

[0014] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make this application thorough and complete and to enable those skilled in the art to fully understand the scope of this application.

[0015] In the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0016] As used herein, the term "and / or" includes any and all combinations of at least one of the associated listed items.

[0017] The terms used herein are only used to describe specific embodiments and are not intended to limit the present application. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of at least one other feature, whole, step, operation, element, component and / or its group is not excluded.

[0018] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this application, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined herein.

[0019] Figure 1 A flowchart of an image denoising method provided by one embodiment of the present application.

[0020] First, refer to Figure 1 An embodiment of the present application provides an image denoising method, comprising:

[0021] Step 100, determining an adaptive smoothing parameter corresponding to the current pixel to be updated according to the local adjustment gain and initial smoothing parameter corresponding to the current pixel to be updated in the target image; wherein the local adjustment gain corresponding to the pixel to be updated that meets different conditions is different.

[0022] In some exemplary embodiments, the local adjustment gain includes at least one of the following: brightness gain, flatness gain, and radial distance gain.

[0023] In some exemplary embodiments, the different local adjustment gains corresponding to the pixels to be updated that meet different conditions include: the brightness gains corresponding to the pixels to be updated with different Y channel pixel values ​​are different. For example, the smaller the Y channel pixel value of the pixel to be updated, the larger the brightness gain corresponding to the pixel to be updated; the larger the Y channel pixel value of the pixel to be updated, the closer the brightness gain corresponding to the pixel to be updated is to 1. The size of the adaptive smoothing parameter is adjusted by the Y channel pixel value, that is, the brightness value of the image, and different degrees of denoising are performed for different brightness values. The smaller the brightness value, the larger the brightness gain, the larger the adaptive smoothing parameter, and the stronger the denoising effect, thereby achieving the purpose of enhancing the denoising effect of dark areas of the image.

[0024] In some exemplary embodiments, the value range corresponding to the Y channel pixel values ​​of all pixel points of the image may be divided into a plurality of sub-ranges, and each sub-range corresponds to a brightness gain.

[0025] In some exemplary embodiments, the different local adjustment gains corresponding to the pixels to be updated that meet different conditions include: the flatness gains corresponding to the pixels to be updated with different flatness are different. For example, the greater the flatness of the pixel to be updated, the greater the flatness gain corresponding to the pixel to be updated; the smaller the flatness of the pixel to be updated, the closer the flatness gain corresponding to the pixel to be updated is to 1. The size of the adaptive smoothing parameter is adjusted by the flatness, and different degrees of denoising are performed for different flatnesses. The greater the flatness, the greater the flatness gain, the greater the adaptive smoothing parameter, and the stronger the denoising effect, thereby achieving the purpose of enhancing the denoising effect of the flat area.

[0026] In some exemplary embodiments, the value range corresponding to the flatness of all pixels of the image can be divided into multiple sub-ranges, and each sub-range corresponds to a flatness gain. For example, the image can be divided into a flat area, a texture area, and an edge area, and different areas correspond to different flatness gains. The flatness gain of the flat area is the largest, the flatness gain of the edge area is the smallest, and the flatness gain of the texture area is between the flatness gain of the flat area and the flatness gain of the edge area.

[0027] In some exemplary embodiments, the different local adjustment gains corresponding to the pixels to be updated that meet different conditions include: the radial distance gains corresponding to the pixels to be updated that are different in distance from the central pixel of the target image are different. For example, the larger the distance between the pixel to be updated and the central pixel of the target image, the larger the radial distance gain corresponding to the pixel to be updated; the smaller the distance between the pixel to be updated and the central pixel of the target image, the closer the radial distance gain corresponding to the pixel to be updated is to 1. Due to the uneven optical refraction of the lens, the light intensity received by the edge area of ​​the sensor is smaller than that in the central area, resulting in dark corners of the image. By adjusting the size of the adaptive smoothing parameter by the distance from the central pixel of the target image, different degrees of denoising are performed for different distances. The larger the distance, the larger the radial distance gain, the larger the adaptive smoothing parameter, and the stronger the denoising effect, thereby achieving the purpose of enhancing the denoising effect of the edge area.

[0028] In some exemplary embodiments, the value range corresponding to the distance between all pixel points of the image and the central pixel point of the target image may be divided into a plurality of sub-ranges, and each sub-range corresponds to a radial distance gain.

[0029] In some exemplary embodiments, determining the adaptive smoothing parameter corresponding to the current pixel to be updated based on the local adjustment gain and the initial smoothing parameter corresponding to the current pixel to be updated in the target image includes: determining the adaptive smoothing parameter corresponding to the current pixel to be updated as the product of the local adjustment gain and the initial smoothing parameter corresponding to the current pixel to be updated.

[0030] In some exemplary embodiments, when the local adjustment gain includes two or more gains, the adaptive smoothing parameter corresponding to the current pixel to be updated is the product of all local adjustment gains and the initial smoothing parameter. For example, when the local adjustment gain includes: brightness gain, flatness gain, radial distance gain, the adaptive smoothing parameter corresponding to the current pixel to be updated is: sigma = h × i gain ×f gain × gain ; Among them, sigma is the adaptive smoothing parameter corresponding to the current pixel to be updated, h is the initial smoothing parameter, i gain is the brightness gain, f gain is the flatness gain, r gain is the radial distance gain.

[0031] Step 101, updating the Y channel pixel value of the current pixel to be updated according to the adaptive smoothing parameter corresponding to the current pixel to be updated to obtain an updated Y channel pixel value of the current pixel to be updated.

[0032] In some exemplary embodiments, updating the Y channel pixel value of the pixel to be updated according to the adaptive smoothing parameter corresponding to the current pixel to be updated to obtain the updated Y channel pixel value of the current pixel to be updated includes: for each pixel in the search box centered on the current pixel to be updated, determining the weight corresponding to the matching neighborhood box centered on the pixel in the search box; updating the Y channel pixel value of the current pixel to be updated according to the weights corresponding to all matching neighborhood boxes to obtain the updated Y channel pixel value of the current pixel to be updated.

[0033] In some exemplary embodiments, determining the weight corresponding to the matching neighborhood box centered on the pixel point in the search box includes: determining the similarity between the central neighborhood box centered on the current pixel point to be updated and the matching neighborhood box centered on each pixel point in the search box; wherein, Figure 2 As shown, the search box is larger than the central neighborhood box, the central neighborhood box is contained in the search box, and the central neighborhood box and the matching neighborhood box are the same size; the weight corresponding to the matching neighborhood box is determined according to the similarity corresponding to the matching neighborhood box and the adaptive smoothing parameter corresponding to the current pixel to be updated.

[0034] In some exemplary embodiments, determining the similarity between a central neighborhood box centered on the current pixel to be updated and a matching neighborhood box centered on each pixel in the search box includes: determining the similarity between the central neighborhood box and the matching neighborhood box as the Euclidean distance between the Y channel pixel value of the pixel in the central neighborhood box and the Y channel pixel value of the pixel in the matching neighborhood box.

[0035] In some exemplary embodiments, determining the similarity between a central neighborhood box centered on the pixel to be updated and a matching neighborhood box centered on each pixel in the search box includes: determining the similarity between the central neighborhood box and the matching neighborhood box as the average of the differences in Y channel pixel values ​​of the pixels at corresponding positions in the central neighborhood box and the matching neighborhood box. For example, according to the formula Calculate the similarity between the central neighborhood box and the matching neighborhood box; where |V(A)-V(B)| is the similarity between the central neighborhood box and the matching neighborhood box, |v(A(i, j))-v(B(i, j))| is the absolute value of the difference between the Y channel pixel values ​​of the pixels at the corresponding positions in the central neighborhood box and the matching neighborhood box, and 2ds+1 is the side length of the central neighborhood box or the matching neighborhood box.

[0036] In some exemplary embodiments, when determining the similarity between the central neighborhood box and the matching neighborhood box, the Y channel pixel values ​​of the pixels in the central neighborhood box and the Y channel pixel values ​​of the pixels in the matching neighborhood box may be unupdated Y channel pixel values ​​or updated Y channel pixel values.

[0037] In some exemplary embodiments, pixel points at corresponding positions in the central neighborhood box and the matching neighborhood box refer to the pixel points whose relative positions in the central neighborhood box and the matching neighborhood box are the same. For example, the pixel point located in the i-th row and j-th column of the central neighborhood box and the pixel point located in the i-th row and j-th column of the matching neighborhood box are the pixel points at corresponding positions in the central neighborhood box and the matching neighborhood box, and the same applies to pixel points at other positions, which will not be repeated here.

[0038] In some exemplary embodiments, determining the weight corresponding to the matching neighborhood box according to the similarity corresponding to the matching neighborhood box and the adaptive smoothing parameter corresponding to the current pixel to be updated includes: The formula calculates the weight corresponding to the matching neighborhood box; where |V(A)-V(B)| is the similarity between the central neighborhood box and the matching neighborhood box, sigma is the adaptive smoothing parameter corresponding to the current pixel to be updated, and sw(B) is the weight corresponding to the matching neighborhood box.

[0039] In some exemplary embodiments, determining the weight corresponding to the matching neighborhood box based on the similarity corresponding to the matching neighborhood box and the adaptive smoothing parameter corresponding to the current pixel to be updated includes: shifting the similarity corresponding to the matching neighborhood box left by A bits; wherein A is an integer greater than or equal to 1; determining the ratio of the similarity after the left shift by A bits and the adaptive smoothing parameter corresponding to the current pixel to be updated; searching for the weight value corresponding to the determined ratio in the correspondence between the preset ratios and weights, and the weight value found is the weight corresponding to the matching neighborhood box.

[0040] In some exemplary embodiments, the value of A can be set according to actual conditions, as long as the ratio of the similarity after left shifting by A bits to the adaptive smoothing parameter corresponding to the current pixel to be updated is an integer, for example, A can be 12.

[0041] In some exemplary embodiments, updating the Y channel pixel value of the current pixel to be updated according to the weights corresponding to all matching neighborhood boxes to obtain the updated Y channel pixel value of the current pixel to be updated includes: determining that the updated Y channel pixel value of the current pixel to be updated is a weighted average of the Y channel pixel values ​​of the central pixels of all matching neighborhood boxes with the weights corresponding to all matching neighborhood boxes as weights.

[0042] In some exemplary embodiments, determining that the updated Y channel pixel value of the current pixel to be updated is the weighted average of the Y channel pixel values ​​of the central pixels of all matching neighborhood boxes with the weights corresponding to all matching neighborhood boxes as weights includes: determining the sum of the weights corresponding to all matching neighborhood boxes; for each matching neighborhood box, determining the product between the Y channel pixel value of the central pixel of the matching neighborhood box and the weight corresponding to the matching neighborhood box; and determining that the ratio of the sum of the products corresponding to all matching neighborhood boxes and the sum of the weights corresponding to all matching neighborhood boxes is the updated Y channel pixel value of the current pixel to be updated.

[0043] That is to say, according to the formula Calculate the updated Y channel pixel value of the current pixel to be updated; where dst(A0) is the updated Y channel pixel value of the center pixel in the center neighborhood box, that is, the updated Y channel pixel value of the current pixel to be updated. sw(B) is the weight corresponding to the matching neighborhood box, src(B0) is the Y channel pixel value of the central pixel of the matching neighborhood box, S represents the search box range, and B∈S represents the matching neighborhood box within the search box range.

[0044] Step 102, another pixel to be updated in the target image is set as the current pixel to be updated, and the above steps are repeated until the Y channel pixel values ​​of all the pixels to be updated in the target image are updated.

[0045] The image denoising method provided in the embodiment of the present application obtains an adaptive smoothing parameter based on a local adjustment gain. Since the local adjustment gains corresponding to the pixels to be updated that meet different conditions are different, the adaptive smoothing parameters corresponding to the pixels to be updated that meet different conditions are also different. As a result, the pixels to be updated that meet different conditions are denoised to different degrees, thereby greatly improving the denoising effect.

[0046] Figure 3 A block diagram of the composition of an image denoising device provided in another embodiment of the present application.

[0047] Second, refer to Figure 3 Another embodiment of the present application provides an image denoising device, including: an adaptive smoothing parameter determination module 301, used to determine the adaptive smoothing parameter corresponding to the current pixel to be updated in the target image according to the local adjustment gain and the initial smoothing parameter corresponding to the current pixel to be updated in the target image; wherein the local adjustment gain corresponding to the pixel to be updated that meets different conditions is different; an updating module 302, used to update the Y channel pixel value of the current pixel to be updated according to the adaptive smoothing parameter corresponding to the current pixel to be updated to obtain the updated Y channel pixel value of the current pixel to be updated; the adaptive smoothing parameter determination module is also used to: set another pixel to be updated in the target image as the current pixel to be updated, and continue to repeat the above steps until the Y channel pixel values ​​of all pixels to be updated in the target image are updated.

[0048] In some exemplary embodiments, the local adjustment gain includes at least one of the following: brightness gain, flatness gain, and radial distance gain.

[0049] In some exemplary embodiments, the smaller the Y channel pixel value of the current pixel to be updated, the larger the brightness gain corresponding to the current pixel to be updated; the larger the Y channel pixel value of the current pixel to be updated, the closer the brightness gain corresponding to the current pixel to be updated is to 1.

[0050] In some exemplary embodiments, the greater the flatness of the pixel to be updated, the greater the flatness gain corresponding to the pixel to be updated; the smaller the flatness of the pixel to be updated, the closer the flatness gain corresponding to the pixel to be updated is to 1.

[0051] In some exemplary embodiments, the greater the distance between the current pixel to be updated and the central pixel of the target image, the greater the radial distance gain corresponding to the current pixel to be updated; the smaller the distance between the current pixel to be updated and the central pixel of the target image, the closer the radial distance gain corresponding to the current pixel to be updated is to 1.

[0052] In some exemplary embodiments, the adaptive smoothing parameter determination module 301 is specifically used to determine the adaptive smoothing parameter corresponding to the current pixel to be updated in the target image according to the local adjustment gain and the initial smoothing parameter corresponding to the current pixel to be updated in the target image in the following manner: the adaptive smoothing parameter corresponding to the current pixel to be updated is determined as the product of the local adjustment gain and the initial smoothing parameter corresponding to the current pixel to be updated.

[0053] In some exemplary embodiments, the update module 302 is specifically used to: determine the weight corresponding to the matching neighborhood box centered on the pixel point in the search box for each pixel point in the search box centered on the current pixel point to be updated; and update the Y channel pixel value of the current pixel point to be updated according to the weights corresponding to all matching neighborhood boxes to obtain the updated Y channel pixel value of the current pixel point to be updated.

[0054] In some exemplary embodiments, the update module 302 is specifically used to determine the weight corresponding to the matching neighborhood box centered on the pixel point in the search box in the following manner: determine the similarity between the central neighborhood box centered on the current pixel point to be updated and the matching neighborhood box centered on the pixel point in the search box; wherein the central neighborhood box is contained in the search box, and the central neighborhood box and the matching neighborhood box have the same size; determine the weight corresponding to the matching neighborhood box according to the similarity corresponding to the matching neighborhood box and the adaptive smoothing parameter corresponding to the current pixel point to be updated.

[0055] In some exemplary embodiments, the updating module 302 is specifically used to determine the weight corresponding to the matching neighborhood box according to the similarity corresponding to the matching neighborhood box and the adaptive smoothing parameter corresponding to the current pixel to be updated in the following manner: shift the similarity corresponding to the matching neighborhood box left by A bits; wherein A is an integer greater than or equal to 1; determine the ratio of the similarity after the left shift by A bits and the adaptive smoothing parameter corresponding to the current pixel to be updated; in the correspondence between the preset ratio and the weight, search for the weight value corresponding to the determined ratio, and the weight value found is the weight corresponding to the matching neighborhood box.

[0056] In some exemplary embodiments, the updating module 302 is specifically used to update the Y channel pixel value of the current pixel to be updated according to the weights corresponding to all matching neighborhood boxes to obtain the updated Y channel pixel value of the current pixel to be updated in the following manner: determine the updated Y channel pixel value of the current pixel to be updated as the weighted average of the Y channel pixel values ​​of the central pixels of all matching neighborhood boxes with the weights corresponding to all matching neighborhood boxes as weights.

[0057] In some exemplary embodiments, the updating module 302 is specifically used to implement the following method to determine that the updated Y channel pixel value of the current pixel to be updated is the weighted average of the Y channel pixel values ​​of the central pixels of all matching neighborhood boxes with the weights corresponding to all matching neighborhood boxes as weights: determine the sum of the weights corresponding to all matching neighborhood boxes; for each matching neighborhood box, determine the product between the Y channel pixel value of the central pixel of the matching neighborhood box and the weight corresponding to the matching neighborhood box; determine that the ratio of the sum of the products corresponding to all matching neighborhood boxes and the sum of the weights corresponding to all matching neighborhood boxes is the updated Y channel pixel value of the current pixel to be updated.

[0058] The specific implementation process of the above-mentioned image denoising device is the same as the specific implementation process of the image denoising method in the aforementioned embodiment, and will not be repeated here.

[0059] Thirdly, refer to Figure 4 Another embodiment of the present application provides an electronic device, including: at least one processor 401; a memory 402, wherein at least one program is stored in the memory 402, and when the at least one program is executed by the at least one processor 401, any one of the above-mentioned image denoising methods is implemented.

[0060] In some exemplary embodiments, the electronic device further includes: one or more I / O interfaces 403 connected between the processor 401 and the memory 402 , and configured to implement information interaction between the processor 401 and the memory 402 .

[0061] Among them, the processor 401 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 402 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 403 is connected between the processor 501 and the memory 502, and can realize information interaction between the processor 401 and the memory 502, including but not limited to a data bus (Bus), etc.

[0062] In some embodiments, the processor 401 , the memory 402 , and the I / O interface 403 are connected to each other via a bus 404 , and further connected to other components of the computing device.

[0063] In a fourth aspect, another embodiment of the present application provides a computer-readable medium having a computer program stored thereon, and when the computer program is executed by a processor, any one of the above-mentioned image denoising methods is implemented.

[0064] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0065] Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for limiting purposes. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly noted, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, those skilled in the art will appreciate that various changes in form and detail may be made without departing from the scope of the present application as set forth in the appended claims.

Claims

1. An image denoising method, comprising: Determining an adaptive smoothing parameter corresponding to the current pixel to be updated according to a local adjustment gain corresponding to the current pixel to be updated in the target image and an initial smoothing parameter; wherein the local adjustment gain corresponding to the pixel to be updated that meets different conditions is different; The Y channel pixel value of the current pixel to be updated is updated according to the adaptive smoothing parameter corresponding to the current pixel to be updated to obtain the updated Y channel pixel value of the current pixel to be updated; Another pixel to be updated in the target image is set as the current pixel to be updated, and the above steps are repeated until the Y channel pixel values ​​of all the pixels to be updated in the target image are updated.

2. The image denoising method according to claim 1, wherein: The local adjustment gain includes at least one of the following: Brightness gain, flatness gain, radial distance gain.

3. The image denoising method according to claim 2, wherein: The smaller the Y channel pixel value of the current pixel to be updated, the larger the brightness gain corresponding to the current pixel to be updated; the larger the Y channel pixel value of the current pixel to be updated, the closer the brightness gain corresponding to the current pixel to be updated is to 1.

4. The image denoising method according to claim 2, wherein: The greater the flatness of the current pixel to be updated, the greater the flatness gain corresponding to the current pixel to be updated; the smaller the flatness of the current pixel to be updated, the closer the flatness gain corresponding to the current pixel to be updated is to 1.

5. The image denoising method according to claim 2, wherein: The greater the distance between the current pixel to be updated and the central pixel of the target image, the greater the radial distance gain corresponding to the current pixel to be updated; the smaller the distance between the current pixel to be updated and the central pixel of the target image, the closer the radial distance gain corresponding to the current pixel to be updated is to 1.

6. The image denoising method according to claim 1, wherein: Determining the adaptive smoothing parameter corresponding to the current pixel to be updated according to the local adjustment gain and the initial smoothing parameter corresponding to the current pixel to be updated in the target image comprises: The adaptive smoothing parameter corresponding to the current pixel to be updated is determined to be the product of the local adjustment gain corresponding to the current pixel to be updated and the initial smoothing parameter.

7. The image denoising method according to claim 1, wherein: The updating of the Y channel pixel value of the pixel to be updated according to the adaptive smoothing parameter corresponding to the current pixel to be updated to obtain the updated Y channel pixel value of the current pixel to be updated comprises: For each pixel point in a search box centered on the current pixel point to be updated, determine a weight corresponding to a matching neighborhood box centered on the pixel point in the search box; The Y channel pixel value of the current pixel to be updated is updated according to the weights corresponding to all the matching neighborhood boxes to obtain the updated Y channel pixel value of the current pixel to be updated.

8. The image denoising method according to claim 7, wherein: Determining the weight corresponding to the matching neighborhood box centered on the pixel point in the search box includes: Determine the similarity between a central neighborhood box centered on the current pixel to be updated and a matching neighborhood box centered on each pixel in the search box; wherein the central neighborhood box is contained in the search box, and the central neighborhood box and the matching neighborhood box have the same size; The weight corresponding to the matching neighborhood box is determined according to the similarity corresponding to the matching neighborhood box and the adaptive smoothing parameter corresponding to the current pixel point to be updated.

9. The image denoising method according to claim 8, wherein: Determining the weight corresponding to the matching neighborhood box according to the similarity corresponding to the matching neighborhood box and the adaptive smoothing parameter corresponding to the current pixel to be updated includes: Shift the similarity corresponding to the matching neighborhood box to the left by A bits; where A is an integer greater than or equal to 1; Determine the ratio of the similarity after left shifting by A bits to the adaptive smoothing parameter corresponding to the current pixel to be updated; In the correspondence between the preset ratios and weights, the weight value corresponding to the determined ratio is searched, and the weight value found is the weight corresponding to the matching neighborhood box.

10. The image denoising method according to claim 7, wherein: The updating of the Y channel pixel value of the current pixel to be updated according to the weights corresponding to all the matching neighborhood boxes to obtain the updated Y channel pixel value of the current pixel to be updated comprises: Determine the updated Y channel pixel value of the current pixel to be updated as a weighted average of the Y channel pixel values ​​of the central pixels of all the matching neighborhood boxes with the weights corresponding to all the matching neighborhood boxes as weights.

11. The image denoising method according to claim 10, wherein: The step of determining the updated Y channel pixel value of the current pixel point to be updated as a weighted average of the Y channel pixel values ​​of the central pixels of all the matching neighborhood boxes with the weights corresponding to all the matching neighborhood boxes as weights comprises: Determine the sum of weights corresponding to all the matching neighborhood boxes; For each of the matching neighborhood boxes, determine the product of the Y channel pixel value of the central pixel point of the matching neighborhood box and the weight corresponding to the matching neighborhood box; Determine the updated Y channel pixel value of the current pixel to be updated as the ratio of the sum of the products corresponding to all the matching neighborhood boxes and the sum of the weights corresponding to all the matching neighborhood boxes.

12. An image denoising device, comprising: An adaptive smoothing parameter determination module, used to determine the adaptive smoothing parameter corresponding to the current pixel to be updated in the target image according to the local adjustment gain corresponding to the current pixel to be updated and the initial smoothing parameter; wherein the local adjustment gain corresponding to the pixel to be updated that meets different conditions is different; An updating module, used for updating the Y channel pixel value of the current pixel to be updated according to the adaptive smoothing parameter corresponding to the current pixel to be updated to obtain an updated Y channel pixel value of the current pixel to be updated; The adaptive smoothing parameter determination module is also used to: set another pixel point to be updated in the target image as the current pixel point to be updated, and continue to repeat the above steps until the Y channel pixel values ​​of all the pixels to be updated in the target image are updated.

13. An electronic device, comprising: at least one processor; A memory, wherein at least one program is stored in the memory, and when the at least one program is executed by the at least one processor, the image denoising method according to any one of claims 1 to 11 is implemented.

14. A computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the image denoising method according to any one of claims 1 to 11.

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