Image denoising method, device, medium and electronic device

Abstract

The present application provides image denoising method, device, medium and electronic equipment. The image denoising method comprises: obtaining a to-be-processed image and selecting a reference frame and a current frame therefrom; performing content alignment processing on the current frame according to the reference frame; calculating the pixel weight of each current frame after alignment processing; accumulating the pixel weight of the current frame to obtain a first weight accumulation map; calculating the pixel weight of the reference frame according to the first weight accumulation map; accumulating the pixel weight of the current frame and the reference frame to obtain a second weight accumulation map; performing weighted summation on the pixel values of the current frame and the reference frame after alignment processing according to the pixel weight of the current frame and the reference frame, to obtain a pixel weight product accumulation map; and obtaining a multi-frame denoising image according to the ratio of the pixels at the same position in the pixel weight product accumulation map and the second weight accumulation map. The method can improve the problem of motion ghosting or excessive noise that is prone to occurring in the traditional multi-frame denoising result.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to an image denoising method, apparatus, medium, and electronic device. Background Technology

[0002] With the continuous development of technology, digital photography has brought great convenience to people's lives. Due to the inherent characteristics of digital photography devices, the resulting images are inevitably affected by noise, so denoising processing is necessary. Compared to single-frame denoising algorithms, which easily blur image details and reduce image quality, multi-frame denoising algorithms can reduce the variance of the random distribution of noise, thereby fundamentally reducing image noise without affecting the inherent content of the image. Therefore, multi-frame image denoising algorithms have become a common and important algorithm for digital image denoising.

[0003] The process of existing multi-frame image denoising algorithms is mostly as follows: capture multiple frames of images to perform frame selection processing to obtain a reference frame and the current frame, align the current frame to the reference frame, calculate the weight of each pixel in the aligned current frame image (block) and the reference frame, perform a weighted average on all aligned current frames and reference frames to obtain a preliminary multi-frame denoising result, and add a post-processing module to the preliminary denoising result to further improve the denoising quality.

[0004] In most multi-frame denoising algorithms, the reference frame weight is usually set to a fixed constant. This setting negatively impacts the initial multi-frame denoising results: if the reference frame weight is set too high, the reference frame component in the final multi-frame denoising result will increase. Since the reference frame itself is an undenoised image containing a large amount of noise, the resulting multi-frame denoising result will inevitably contain significant noise. Conversely, if the reference frame weight is set too low, there will be no dominant image frame content near areas with local motion, inevitably resulting in noticeable ghosting. In summary, existing multi-frame denoising algorithms inevitably produce images with motion ghosting or excessive noise. Therefore, it is necessary to propose a new multi-frame image denoising method to address these issues. Summary of the Invention

[0005] The purpose of this invention is to provide an image denoising method, apparatus, medium, and electronic device to improve the problems of motion ghosting or excessive noise that easily occur in traditional multi-frame denoising results.

[0006] In a first aspect, the image denoising method provided by the present invention includes: acquiring multiple frames of images to be processed captured by a camera for the same scene; selecting a reference frame and n current frames from the multiple frames of images to be processed, where n is a positive integer; performing content alignment processing on the n current frames according to the reference frame; calculating the similarity between the pixel value of each current frame after alignment processing and the corresponding pixel value in the reference frame, and obtaining the pixel weight of each current frame, wherein the higher the similarity, the greater the pixel weight; accumulating the pixel weights of pixels at the same position in the n current frames to obtain a first weight accumulation map; and calculating the weight of each pixel at each position in the first weight accumulation map. The pixel weights of the reference frame are calculated using a summation value. A smaller summation value indicates a larger pixel weight at the specified position in the reference frame, and vice versa. The pixel weights of pixels at the same position in the n current frames and the reference frame are summed to obtain a second weight accumulation map. Based on the pixel weights of the n aligned current frames and the reference frame, a weighted summation of the pixel values ​​of the n aligned current frames and the reference frame is obtained to obtain a pixel weight multiplication accumulation map. The multi-frame denoised image is obtained by comparing the pixel weight multiplication accumulation map with the pixel weight accumulation map at the same position in the second weight accumulation map.

[0007] The beneficial effects of the method provided by this invention are as follows: it can adaptively calculate the weight of each pixel in the reference frame according to the image content, thereby improving the problems of motion ghosting or excessive noise that easily occur in traditional multi-frame denoising results. The multi-frame denoised image obtained by using this method will not have excessive noise or strong motion ghosting across the entire image range, thus making it easier to perform post-processing on the image to obtain a better result image.

[0008] In one possible embodiment, the pixel weight of the reference frame is calculated based on the sum of the weights and values ​​of pixels at each location in the first weighted cumulative image, including:

[0009] Pixel by pixel, determine whether the sum of the weights of each pixel in the first weighted cumulative image is less than a preset threshold;

[0010] If the pixel weights are greater than or equal to a preset threshold, the pixel weights of the reference frame satisfy the following formula:

[0011]

[0012] Wherein, refweight(i,j) represents the pixel weight of the pixel at position (i,j) in the reference frame, sumweight(i,j) represents the sum of the weights of the pixel at position (i,j) in the first weight accumulation map, σ represents the adjustable attenuation coefficient corresponding to the scene, th1 represents the preset threshold, and i and j are positive integers.

[0013] In another possible embodiment, calculating the pixel weight of the reference frame based on the weight and value of each pixel in the first weighted summation map further includes:

[0014] If the pixel weight is less than the preset threshold, the pixel weight of the reference frame satisfies the following formula:

[0015] refweight(i, j) = const 1;

[0016] Where const1 is a preset constant.

[0017] In other possible embodiments, selecting a reference frame and n current frames from the multiple frames of images to be processed includes:

[0018] According to the preset criteria corresponding to the scenario, multiple frames of images to be processed are filtered, discarding image frames that do not meet the preset criteria and retaining image frames that meet the preset criteria.

[0019] Select one frame from the reserved image frames that meet the preset criteria as the reference frame, and use the remaining n frames as the current frames.

[0020] Based on the pixel weights of the current frame after n alignment processing and the pixel weights of the reference frame, a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing is performed to obtain a pixel weight multiplicative summation image, including:

[0021] For pixels at the same position: multiply the pixel values ​​of each current frame after n alignment processing with the corresponding pixel weights to obtain n first values, and multiply the pixel values ​​of the reference frame with the corresponding pixel weights to obtain second values. Accumulate the n first values ​​and the second values ​​to obtain the weighted cumulative sum of the pixels at the position. The weighted cumulative sum of the pixels at all positions forms a pixel weighted cumulative sum map.

[0022] Secondly, the present invention also provides an image denoising apparatus, the apparatus comprising:

[0023] The acquisition unit is used to acquire multiple frames of images to be processed captured by the camera for the same scene;

[0024] The selection unit is used to select a reference frame and n current frames from the multiple frames of images to be processed, where n is a positive integer;

[0025] An alignment unit is used to perform content alignment processing on the n current frames according to the reference frame;

[0026] The current frame weight calculation unit is used to calculate the similarity between the pixel value of each current frame after alignment processing and the corresponding pixel value in the reference frame, so as to obtain the pixel weight of each current frame. The higher the similarity, the greater the pixel weight.

[0027] The first accumulation unit is used to accumulate the pixel weights of n pixels at the same position in the current frame to obtain the first weight accumulation map;

[0028] The reference frame weight calculation unit is used to calculate the pixel weight of the reference frame based on the weight sum of each pixel in the first weight accumulation image, wherein the smaller the weight sum, the larger the pixel weight value of the pixel at the specified position in the reference frame; and the larger the weight sum, the smaller the pixel weight value of the pixel at the specified position in the reference frame.

[0029] The second accumulation unit is used to accumulate the pixel weights of the same position pixels in the n current frames and the reference frame to obtain the second weight accumulation map;

[0030] The pixel weight product calculation unit is used to perform a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing based on the pixel weights of the current frame after n alignment processing and the pixel weights of the reference frame, so as to obtain a pixel weight product summation map.

[0031] The denoising unit is used to obtain multiple frames of denoised images by multiplying the pixel weights by the ratio of pixels at the same position in the accumulated image and the second weighted accumulated image.

[0032] The reference frame weight calculation unit calculates the pixel weight of the reference frame based on the weight and value of each pixel in the first weight accumulation image, specifically for:

[0033] Each pixel is checked to determine whether the sum of the weights of each pixel in the first weighted cumulative image is less than a preset threshold.

[0034] If the pixel weights are greater than or equal to a preset threshold, the pixel weights of the reference frame satisfy the following formula:

[0035]

[0036] Wherein, refweight(i,j) represents the pixel weight of the pixel at position (i,j) in the reference frame, sumweight(i,j) represents the sum of the weights of the pixel at position (i,j) in the first weight accumulation map, σ represents the adjustable attenuation coefficient corresponding to the scene, th1 represents the preset threshold, and i and j are positive integers.

[0037] The reference frame weight calculation unit calculates the pixel weight of the reference frame based on the weight and value of each pixel in the first weight accumulation image, specifically for:

[0038] If the pixel weight is less than the preset threshold, the pixel weight of the reference frame satisfies the following formula:

[0039] refweight(i, j) = const 1;

[0040] Where const1 is a preset constant.

[0041] The selection unit selects one reference frame and n current frames from the multiple frames of images to be processed, specifically for:

[0042] According to the preset criteria corresponding to the scenario, multiple frames of images to be processed are filtered, discarding image frames that do not meet the preset criteria and retaining image frames that meet the preset criteria.

[0043] Select one frame from the reserved image frames that meet the preset criteria as the reference frame, and use the remaining n frames as the current frames.

[0044] The pixel weight product calculation unit performs a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing steps, based on the pixel weights of the current frame and the reference frame, to obtain a pixel weight product summation image, specifically used for:

[0045] For pixels at the same position: multiply the pixel values ​​of each current frame after n alignment processing with the corresponding pixel weights to obtain n first values, and multiply the pixel values ​​of the reference frame with the corresponding pixel weights to obtain second values. Accumulate the n first values ​​and the second values ​​to obtain the weighted cumulative sum of the pixels at the position. The weighted cumulative sum of the pixels at all positions forms a pixel weighted cumulative sum map.

[0046] Thirdly, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described image denoising method.

[0047] Fourthly, the present invention also provides an electronic device, comprising: a processor and a memory; the memory being used to store a computer program; the processor being used to execute the computer program stored in the memory, so that the electronic device performs the above-described image denoising method.

[0048] For the beneficial effects of the second to fourth aspects mentioned above, please refer to the description of the first aspect mentioned above. Attached Figure Description

[0049] Figure 1 This is a schematic flowchart of an image denoising method provided in an embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of an image denoising device provided in an embodiment of the present invention;

[0051] Figure 3 This is a schematic diagram of an electronic device structure provided in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed following the word and its equivalents, but do not exclude other elements or objects.

[0053] To address the problems existing in the prior art, embodiments of the present invention provide a method, apparatus, medium, and electronic device for image denoising.

[0054] This embodiment provides a method for image denoising; see the appendix to the specification. Figure 1 The method includes:

[0055] S101: Acquire multiple frames of images to be processed from the same scene captured by the camera.

[0056] S102: Select a reference frame and n current frames from multiple frames of images to be processed, where n is a positive integer.

[0057] In S102, in one possible embodiment, selecting a reference frame and n current frames from multiple frames of images to be processed specifically includes: filtering the multiple frames of images to be processed according to a preset standard corresponding to the scene, discarding image frames that do not meet the preset standard, and retaining image frames that meet the preset standard; selecting one frame from the retained image frames that meet the preset standard as a reference frame, and using the remaining n frames as current frames.

[0058] In a specific embodiment, the preset standard corresponding to the scene is the image sharpness level. Image frames that do not meet the sharpness standard are discarded, and image frames that meet the sharpness standard are retained. The frame with the highest sharpness among the retained image frames is selected as the reference frame, and the remaining n image frames are used as the current frames, where n>1.

[0059] S103: Based on the reference frame, perform content alignment processing on n current frames.

[0060] In S103, in one possible embodiment, the alignment algorithm includes, but is not limited to, global alignment methods, local alignment methods, and optical flow methods. In the specific processing, the result obtained after the current frame is aligned can be an entire image aligned to the reference frame, or it can be an aligned image block. Subsequent operations all involve calculating the weights of pixels within the aligned image (block).

[0061] S104: Calculate the similarity between the pixel value of each current frame after alignment and the corresponding pixel value in the reference frame to obtain the pixel weight of each current frame. The higher the similarity, the greater the pixel weight.

[0062] In S104, in one possible embodiment, when calculating the pixel weights of the current frame, any weight calculation method used in multi-frame fusion can be used.

[0063] S105: Accumulate the pixel weights of pixels at the same position in n current frames to obtain a first weight accumulation map. Specifically, for the same pixel position, accumulate the pixel weights of each of the n aligned current frames to obtain the accumulated weight value of the pixel at that position. The accumulated weight values ​​of pixels at all positions form the first weight accumulation map. The first weight accumulation map is the weight accumulation map of n aligned current frames, which does not include the weights of the reference frame.

[0064] S106: Calculate the pixel weight of the reference frame based on the sum of the weights of each pixel in the first weighted sum image. Specifically, a smaller sum of weights results in a larger pixel weight at the same position in the reference frame; conversely, a larger sum of weights results in a smaller pixel weight at the same position in the reference frame.

[0065] In S106, in one possible embodiment, calculating the pixel weight of the reference frame based on the weight sum of each pixel in the first weighted cumulative image includes: determining, pixel by pixel, whether the weight sum of each pixel in the first weighted cumulative image is less than a preset threshold. For a pixel position, if the weight sum of the pixel at that position is greater than or equal to the preset threshold, then the pixel weight of the reference frame satisfies the following formula: Where refweight(i,j) represents the pixel weight of the pixel at position (i,j) in the reference frame, sumweight(i,j) represents the sum of the pixel weights at position (i,j) in the first weight accumulation map, σ represents the adjustable attenuation coefficient corresponding to the scene, th1 represents the preset threshold, and i and j are positive integers. If the sum of the pixel weights at this position is less than the preset threshold, then the pixel weights of the reference frame satisfy the following formula: refweight(i,j) = const1; where const1 is a preset constant.

[0066] In one possible embodiment, the preset threshold is determined based on the maximum value of the first weighted cumulative graph and a scaling factor. Specifically, the preset threshold satisfies the following formula: th1 = max(sumweight) × α; where max(sumweight) is the maximum value of the first weighted cumulative graph, and α is a scaling factor, which is a decimal.

[0067] In one possible embodiment, const1 is determined by multiplying the maximum value of the first weighted cumulative graph by a certain decimal.

[0068] In one specific embodiment, the decimal is Right now

[0069] In one possible embodiment, the value of σ is determined according to... It changes according to the preset attenuation level.

[0070] In a specific embodiment, when sumweight = 2th1, Top-down attenuation to Therefore, the value of σ is determined accordingly. Furthermore, in other embodiments, The preset attenuation level will vary, and the value of σ will also change accordingly.

[0071] In a specific embodiment, max(sumweight) is the maximum value that sumweight can take. Specifically, it is calculated using various weight calculation methods used in multi-frame fusion to obtain the maximum value of the first weight accumulation map. The values ​​of the first weight accumulation map calculated using different methods are different, and the maximum value is the maximum value that sumweight can take, which is the max(sumweight) value used to determine the preset threshold and σ in this embodiment.

[0072] S107: Accumulate the pixel weights of the same position pixels in the current frame and the reference frame to obtain the second weight accumulation map.

[0073] S108: Based on the pixel weights of the current frame after n alignment processing and the pixel weights of the reference frame, perform a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing to obtain a pixel weight multiplication cumulative summation image.

[0074] In S108, in one possible embodiment, based on the pixel weights of the current frame after n alignment processing and the pixel weights of the reference frame, a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing is performed to obtain a pixel weight multiplicative summation map, including:

[0075] For pixels at the same position: multiply the pixel values ​​of each current frame after n alignment processing with the corresponding pixel weights to obtain n first values, and multiply the pixel values ​​of the reference frame with the corresponding pixel weights to obtain second values. Accumulate the n first values ​​and the second values ​​to obtain the weighted cumulative sum of the pixels at that position. The weighted cumulative sum of the pixels at all positions forms a pixel weighted cumulative sum map.

[0076] S109: Obtain multi-frame denoised images by multiplying the pixel weights by the ratio of pixels at the same position in the cumulative image and the second weighted cumulative image.

[0077] In S109, in one possible embodiment, the pixel weights at each position of the accumulated image are multiplied by the accumulated value and divided by the weight accumulation value at the same position to obtain a multi-frame denoised image.

[0078] The image denoising method of this invention proposes a method for adaptively calculating the weight of each pixel in the reference frame based on the image content, in order to improve the problems of motion ghosting or excessive noise that easily occur in traditional multi-frame denoising results. The multi-frame denoised images obtained by using this method do not contain excessive noise or strong motion ghosting across the entire image, thus making it more conducive to post-processing of the image to obtain better results.

[0079] See the instruction manual appendix Figure 2 This embodiment provides an image denoising device for implementing the method described in Embodiment 1. The device includes:

[0080] The acquisition unit 201 is used to acquire multiple frames of images to be processed obtained by the camera for the same scene.

[0081] The selection unit 202 is used to select a reference frame and n current frames from multiple frames of images to be processed, where n is a positive integer.

[0082] Alignment unit 203 is used to perform content alignment processing on n current frames based on the reference frame.

[0083] The current frame weight calculation unit 204 is used to calculate the similarity between the pixel value of each current frame after alignment processing and the corresponding pixel value in the reference frame, so as to obtain the pixel weight of each current frame. The higher the similarity, the greater the pixel weight.

[0084] The first accumulation unit 205 is used to accumulate the pixel weights of n pixels at the same position in the current frame to obtain the first weight accumulation map.

[0085] The reference frame weight calculation unit 206 is used to calculate the pixel weight of the reference frame based on the weight sum of each pixel in the first weight accumulation image. The smaller the weight sum, the larger the pixel weight of the pixel at the same position in the reference frame; the larger the weight sum, the smaller the pixel weight of the pixel at the same position in the reference frame.

[0086] The second accumulation unit 207 is used to accumulate the pixel weights of the same position pixels in the current frame and the reference frame to obtain the second weight accumulation map.

[0087] The pixel weight product calculation unit 208 is used to perform a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing based on the pixel weights of the current frame and the reference frame after n alignment processing, so as to obtain a pixel weight product summation map.

[0088] The denoising unit 209 is used to obtain multiple frames of denoised images by multiplying the pixel weights of the accumulated image and the pixel weights of the second accumulated image at the same position.

[0089] In other embodiments of this application, an electronic device is disclosed, such as... Figure 3 As shown, the electronic device may include: one or more processors 301; a memory 302; a display 303; one or more application programs (not shown); and one or more computer programs 304. These devices can be connected via one or more communication buses 305. The one or more computer programs 304 are stored in the memory 302 and configured to be executed by the one or more processors 301. The one or more computer programs 304 include instructions that can be used to perform actions such as... Figure 1 Each step in the corresponding embodiment.

[0090] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] In the embodiments of this application, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0092] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as flash memory, portable hard disk, read-only memory, random access memory, magnetic disk, or optical disk.

[0093] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

Claims

1. An image denoising method, characterized in that, The method includes: Acquire multiple frames of images to be processed from the same scene captured by the camera; Select one reference frame and n current frames from the multiple frames of images to be processed, where n is a positive integer; Based on the reference frame, the n current frames are subjected to content alignment processing; The similarity between the pixel value of each current frame after alignment processing and the corresponding pixel value in the reference frame is calculated to obtain the pixel weight of each current frame. The higher the similarity, the greater the pixel weight. The pixel weights of the same position pixels in the n current frames are accumulated to obtain the first weight accumulation map; Based on the weight and value of each pixel in the first weighted cumulative image, the pixel weight of the reference frame is calculated, including: Pixel by pixel, determine whether the sum of the weights of each pixel in the first weighted cumulative image is less than a preset threshold; If the pixel weights are greater than or equal to a preset threshold, the pixel weights of the reference frame satisfy the following formula: ； If the pixel weight is less than the preset threshold, the pixel weight of the reference frame satisfies the following formula: = const1; in, This represents the pixel weight of the pixel at position (i, j) in the reference frame. This represents the sum of the weights of the pixel at position (i, j) in the first weighted cumulative image. This represents the adjustable attenuation coefficient corresponding to the scenario described. This represents the preset threshold, where i and j are positive integers; const1 is a preset constant; The smaller the sum of weights, the larger the pixel weight value of the pixel at the specified position in the reference frame; the larger the sum of weights, the smaller the pixel weight value of the pixel at the specified position in the reference frame. The pixel weights of pixels at the same position in n current frames and the reference frame are accumulated to obtain a second weight accumulation map; Based on the pixel weights of the current frame after n alignment processing and the pixel weights of the reference frame, the pixel values ​​of the current frame and the reference frame after n alignment processing are weighted and summed to obtain a pixel weight multiplication cumulative summation image; Based on the ratio of pixels at the same position in the pixel weight accumulation map and the second weight accumulation map, a multi-frame denoised image is obtained.

2. The method according to claim 1, characterized in that, Selecting a reference frame and n current frames from the multiple frames of images to be processed includes: According to the preset criteria corresponding to the scenario, multiple frames of images to be processed are filtered, discarding image frames that do not meet the preset criteria and retaining image frames that meet the preset criteria. Select one frame from the reserved image frames that meet the preset criteria as the reference frame, and use the remaining n frames as the current frames.

3. The method according to claim 1, characterized in that, Based on the pixel weights of the current frame after n alignment processing and the pixel weights of the reference frame, a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing is performed to obtain a pixel weight multiplicative summation image, including: For pixels at the same position: multiply the pixel values ​​of each current frame after n alignment processing with the corresponding pixel weights to obtain n first values, and multiply the pixel values ​​of the reference frame with the corresponding pixel weights to obtain second values. Accumulate the n first values ​​and the second values ​​to obtain the weighted cumulative sum of the pixels at the position. The weighted cumulative sum of the pixels at all positions forms a pixel weighted cumulative sum map.

4. An image denoising device, characterized in that, The device includes: The acquisition unit is used to acquire multiple frames of images to be processed captured by the camera for the same scene; The selection unit is used to select a reference frame and n current frames from the multiple frames of images to be processed, where n is a positive integer; An alignment unit is used to perform content alignment processing on the n current frames according to the reference frame; The current frame weight calculation unit is used to calculate the similarity between the pixel value of each current frame after alignment processing and the corresponding pixel value in the reference frame, so as to obtain the pixel weight of each current frame. The higher the similarity, the greater the pixel weight. The first accumulation unit is used to accumulate the pixel weights of n pixels at the same position in the current frame to obtain the first weight accumulation map; The reference frame weight calculation unit is used to calculate the pixel weight of the reference frame based on the weight and value of each pixel in the first weight accumulation image, specifically for: Each pixel is checked to determine whether the sum of the weights of each pixel in the first weighted cumulative image is less than a preset threshold. If the pixel weights are greater than or equal to a preset threshold, the pixel weights of the reference frame satisfy the following formula: ； If the pixel weight is less than the preset threshold, the pixel weight of the reference frame satisfies the following formula: = const1; in, This represents the pixel weight of the pixel at position (i, j) in the reference frame. This represents the sum of the weights of the pixel at position (i, j) in the first weighted cumulative image. This represents the adjustable attenuation coefficient corresponding to the scenario described. This represents the preset threshold, where i and j are positive integers; const1 is a preset constant; The smaller the sum of weights, the larger the pixel weight value of the pixel at the specified position in the reference frame; the larger the sum of weights, the smaller the pixel weight value of the pixel at the specified position in the reference frame. The second accumulation unit is used to accumulate the pixel weights of the same position pixels in the n current frames and the reference frame to obtain the second weight accumulation map; The pixel weight product calculation unit is used to perform a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing based on the pixel weights of the current frame after n alignment processing and the pixel weights of the reference frame, so as to obtain a pixel weight product summation map. The denoising unit is used to obtain multiple frames of denoised images by multiplying the pixel weights by the ratio of pixels at the same position in the accumulated image and the second weighted accumulated image.

5. The apparatus according to claim 4, characterized in that, The selection unit selects one reference frame and n current frames from the multiple frames of images to be processed, specifically for: According to the preset criteria corresponding to the scenario, multiple frames of images to be processed are filtered, discarding image frames that do not meet the preset criteria and retaining image frames that meet the preset criteria. Select one frame from the reserved image frames that meet the preset criteria as the reference frame, and use the remaining n frames as the current frames.

6. The apparatus according to claim 4, characterized in that, The pixel weight product calculation unit performs a weighted summation of the pixel values ​​of the current frame and the reference frame after n alignment processing steps, based on the pixel weights of the current frame and the reference frame, to obtain a pixel weight product summation image, specifically used for: For pixels at the same position: multiply the pixel values ​​of each current frame after n alignment processing with the corresponding pixel weights to obtain n first values, and multiply the pixel values ​​of the reference frame with the corresponding pixel weights to obtain second values. Accumulate the n first values ​​and the second values ​​to obtain the weighted cumulative sum of the pixels at the position. The weighted cumulative sum of the pixels at all positions forms a pixel weighted cumulative sum map.

7. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by the processor, it implements the image denoising method according to any one of claims 1 to 3.

8. An electronic device, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to cause the electronic device to perform the image denoising method according to any one of claims 1 to 3.

Images