Image noise reduction method, device, electronic device and storage medium

Abstract

The present application relates to an image denoising method, device, electronic device and storage medium. When performing image denoising, the present application comprehensively adopts filtering and denoising windows of multiple scales (sizes) to filter and denoise the original image. Based on this method, the distribution information of the original image under denoising windows of different scales can be obtained. A large-scale window can be combined to obtain a better denoising effect, and a small-scale window can be combined to keep the image information from being lost, especially avoiding the loss of high-frequency information such as the edge, details, and texture of the image, thereby taking into account both the denoising effect and information protection of the image, avoiding the over-smoothing problem caused by image denoising, and further improving the image quality after denoising.

Description

Technical Field

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

[0002] In image denoising processing, image denoising is generally achieved through spatial domain filtering or frequency domain filtering to reduce noise and artifacts in the image and improve image quality.

[0003] Among them, spatial domain filtering is a method of image filtering based on the spatial relationship between a pixel and its surrounding neighboring pixels through convolution operations. Frequency domain filtering is a type of transform domain filtering, which means first transforming the image to the frequency domain space, processing (filtering) the image in the frequency domain space, and then performing an inverse transform after processing to obtain the filtered image.

[0004] In the process of image denoising based on spatial domain filtering, traditional technology selects a fixed-size window (such as 3×3) and uses the fixed-size window to filter and denoise the image. However, this method is prone to over-smoothing the image while denoising, making the image blurred and causing loss of image information, especially at the edges, details and textures of the image. It is difficult to maintain high-frequency information such as the edges, details or textures of the image, which will in turn affect the quality of the denoised image and reduce its clarity. Similarly, the frequency domain filtering method will also cause the loss of information such as the edges of the image while denoising, making the image blurred. Summary of the invention

[0005] In view of this, the present application provides an image denoising method, device, electronic device and storage medium, which filter and denoise the image by comprehensively adopting filtering and denoising windows of multiple scales (sizes), so that while reducing the noise, the image information (such as high-frequency information such as edges, details, textures, etc.) is not lost as much as possible, avoiding the over-smoothing problem caused by image denoising and improving the image quality after denoising.

[0006] The specific technical solutions are as follows:

[0007] An image noise reduction method, comprising:

[0008] Obtain the original image to be processed;

[0009] denoising the pixels in the original image using a plurality of filtering denoising windows respectively, to obtain denoising sub-results of the pixels corresponding to different filtering denoising windows respectively; different filtering denoising windows correspond to different window scales;

[0010] According to the denoising sub-results of the pixel points in the original image corresponding to different filtering denoising windows, the denoising results corresponding to the pixel points are obtained by processing;

[0011] The noise reduction results corresponding to the pixels in the original image constitute a noise reduction image of the original image.

[0012] Optionally, the denoising process is performed on the pixels in the original image using a plurality of filtering denoising windows respectively to obtain denoising sub-results corresponding to the pixels in different filtering denoising windows respectively, including:

[0013] Sliding each filtering and denoising window in the pixel space provided by the original image;

[0014] For each current position of the filtering and denoising window during the sliding process, pixel value processing is performed on each pixel point in the filtering and denoising window that slides to the current position in the original image to obtain a target pixel value;

[0015] Among them, the target pixel value is the denoising sub-result of the central pixel point in the filtering denoising window of the current position; the denoising sub-results of the pixel point corresponding to different filtering denoising windows include: the target pixel values ​​corresponding to different filtering denoising windows are obtained by performing the pixel value processing on different groups of pixel points corresponding to different filtering denoising windows centered on the pixel point; a group of pixel points corresponding to a filtering denoising window includes each pixel point in the original image that is in the filtering denoising window.

[0016] Optionally, the processing to obtain the denoising result corresponding to the pixel point according to the pixel points in the original image corresponding to the denoising sub-results of different filtering denoising windows respectively includes:

[0017] Get the weights assigned to different filtering and denoising windows respectively;

[0018] Based on the weights respectively assigned to different filtering denoising windows, different denoising sub-results corresponding to the pixel points are weightedly processed to obtain the denoising result corresponding to the pixel points.

[0019] Optionally, the weight corresponding to the filtering and denoising window is negatively correlated with the scale of the filtering and denoising window.

[0020] Optionally, performing weighted processing on different denoising sub-results corresponding to the pixel point based on weights respectively assigned to different filtering denoising windows to obtain the denoising result corresponding to the pixel point includes:

[0021] Use the following formula to calculate the noise reduction result corresponding to the pixel:

[0022]

[0023] in:

[0024] V Indicates the noise reduction result corresponding to the pixel point; V1 , V2 ... Vn They represent different denoising sub-results corresponding to different filtering denoising windows for the pixel points; r1 , r2 ... rn They represent different window radii corresponding to different filter denoising windows; n represents the number of filter denoising windows;

[0025] They represent different weights corresponding to different filtering and denoising windows.

[0026] An image noise reduction device, comprising:

[0027] An acquisition unit, used for acquiring an original image to be processed;

[0028] A first processing unit is used to perform denoising processing on the pixel points in the original image using a plurality of filtering denoising windows respectively, to obtain denoising sub-results corresponding to the pixel points in different filtering denoising windows respectively; different filtering denoising windows correspond to different window scales;

[0029] A second processing unit is used to process the denoising sub-results corresponding to different filtering denoising windows of the pixel points in the original image to obtain the denoising results corresponding to the pixel points;

[0030] The noise reduction results corresponding to the pixels in the original image constitute a noise reduction image of the original image.

[0031] Optionally, the first processing unit is specifically configured to:

[0032] Sliding each filtering and denoising window in the pixel space provided by the original image;

[0033] For each current position of the filtering and denoising window during the sliding process, pixel value processing is performed on each pixel point in the filtering and denoising window that slides to the current position in the original image to obtain a target pixel value;

[0034] Among them, the target pixel value is the denoising sub-result of the central pixel point in the filtering denoising window of the current position; the denoising sub-results of the pixel point corresponding to different filtering denoising windows include: the target pixel values ​​corresponding to different filtering denoising windows are obtained by performing the pixel value processing on different groups of pixel points corresponding to different filtering denoising windows centered on the pixel point; a group of pixel points corresponding to a filtering denoising window includes each pixel point in the original image that is in the filtering denoising window.

[0035] Optionally, the second processing unit is specifically configured to:

[0036] Get the weights assigned to different filtering and denoising windows respectively;

[0037] Based on the weights respectively assigned to different filtering denoising windows, different denoising sub-results corresponding to the pixel points are weightedly processed to obtain the denoising result corresponding to the pixel points.

[0038] Optionally, the weight corresponding to the filtering and denoising window is negatively correlated with the scale of the filtering and denoising window.

[0039] Optionally, the second processing unit performs weighted processing on different denoising sub-results corresponding to the pixel point based on weights respectively assigned to different filtering denoising windows to obtain the denoising result corresponding to the pixel point, including:

[0040] Use the following formula to calculate the noise reduction result corresponding to the pixel:

[0041]

[0042] in:

[0043] V Indicates the noise reduction result corresponding to the pixel point; V1 , V2 ... Vn They represent different denoising sub-results corresponding to different filtering denoising windows for the pixel points; r1 , r2 ... rn They represent different window radii corresponding to different filter denoising windows; n represents the number of filter denoising windows;

[0044] They represent different weights corresponding to different filtering and denoising windows.

[0045] An electronic device, comprising:

[0046] Memory, used to store computer instruction sets;

[0047] A processor is used to implement the image denoising method as described in any one of the above items by executing an instruction set stored in the memory.

[0048] A computer-readable storage medium stores a computer instruction set, wherein the computer instruction set implements the image denoising method as described above when executed by a processor.

[0049] The image denoising method, device, electronic device and storage medium provided in the embodiments of the present application, when performing image denoising, comprehensively adopts filtering and denoising windows of multiple scales (sizes) to filter and denoise the original image. Based on this method, the distribution information of the original image under denoising windows of different scales can be obtained, and a large-scale window can be combined to obtain a better denoising effect, and a small-scale window can be combined to keep the image information from being lost, especially avoiding the loss of high-frequency information such as the edge, details, and texture of the image, thereby taking into account both the denoising effect and information protection of the image, avoiding the over-smoothing problem caused by image denoising, and further improving the image quality after denoising. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0051] Figure 1 is a flow chart of an image noise reduction method provided in an embodiment of the present application;

[0052] Figure 2 (a)- Figure 2 (b) is a schematic diagram of a multi-scale filtering noise reduction window with a pyramid-shaped and inverted pyramid-shaped structure provided in an embodiment of the present application;

[0053] Figure 3 It is a schematic diagram of a process of performing noise reduction processing on pixel points in an original image based on a multi-scale filtering noise reduction window provided in an embodiment of the present application;

[0054] Figure 4(a)-Figure 4(c) It is a schematic diagram of sliding filtering and denoising windows of different scales in the pixel space of the original image provided by an embodiment of the present application;

[0055] Figure 5 It is a schematic diagram of synchronously sliding various filtering and denoising windows in the pixel space of the original image provided by an embodiment of the present application;

[0056] Figure 6is a structural schematic diagram of an image noise reduction device provided in an embodiment of the present application;

[0057] Figure 7 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] When using the existing spatial domain filtering and denoising method to filter and denoise an image based on a fixed-size window, the larger the selected window size, the better the denoising effect, but at the same time it will cause the loss of image information, especially high-frequency information such as image edges, details and textures. On the contrary, the smaller the window size, the more it can avoid the loss of this information as much as possible, but it will lead to a reduced denoising effect and an unsatisfactory denoising effect. The traditional approach is generally to select a window of appropriate size to balance the denoising effect and the protection of image information as much as possible. However, the actual effect of this method is not ideal, and it is difficult to balance the denoising effect and the protection of image information at the same time.

[0060] In response to this problem, the present application focuses on the purpose of reducing the blurring of image information, especially high-frequency information of the image, caused by the noise reduction process, and provides an image noise reduction method, device, electronic device and storage medium. The image noise reduction method, device, electronic device and storage medium perform noise reduction filtering on the image by comprehensively adopting filtering noise reduction windows of multiple scales to take into account the advantages of large-size window denoising and the advantages of small-size window protection of image information such as image edges, details, textures and other information, so as to reduce noise while ensuring that image information, especially high-frequency information such as edges, details, textures and other information is not lost as much as possible.

[0061] In the embodiments of the present application, the scale of the filtering and denoising window refers to the size of the filtering and denoising window, such as 3×3, 5×5, 7×7, etc., and the window mentioned below refers to the filtering and denoising window. In addition, in the implementation of the present application, for other description parts except the "physical edge of the image", the "edge" mentioned specifically refers to the content edge of the image, such as the edge contour of a face in the image.

[0062] See also Figure 1, showing a flow chart of the image denoising method provided in an embodiment of the present application. The image denoising method in an embodiment of the present application can be applied to but not limited to terminal devices such as mobile phones, tablet computers, personal PCs (such as notebooks, all-in-one computers, desktop computers) with image processing functions, or physical machines corresponding to private cloud / public cloud platforms, servers (image processing servers), etc. with image processing functions.

[0063] like Figure 1 As shown, in this embodiment, the image noise reduction method includes the following processing steps:

[0064] Step 101: Obtain an original image to be processed.

[0065] The original image to be processed is an image with noise reduction requirements to be processed for noise reduction. In practical applications, the original image can be at least one image captured in real time in a single-camera / multi-camera image shooting scene, or it can also be an image obtained from a server or network, or it can also be a corresponding image retrieved from a device album / atlas. Of course, it can also be a combination of multiple images obtained from any of the above different sources, without limitation here.

[0066] Step 102: denoising the pixels in the original image using a plurality of filtering denoising windows to obtain denoising sub-results of the pixels corresponding to different filtering denoising windows; different filtering denoising windows correspond to different window scales.

[0067] That is, in essence, the embodiment of the present application uses a set of filtering and denoising windows to perform denoising on the original image, and the set of filtering and denoising windows includes a plurality of filtering and denoising windows corresponding to different scales. For example, the set of filtering and denoising windows may include a plurality of windows with increasing scales in a pyramidal structure, such as Figure 2 (a) shows the 3×3, 5×5, and 7×7 windows, or may further include multiple windows with successively decreasing scales in an inverted pyramid structure, such as Figure 2 (b) shows 7×7, 5×5, and 3×3 windows.

[0068] When a group of filtering denoising windows is used to denoise the original image, each filtering denoising window in the group is used to denoise each pixel in the original image. The denoising process of the pixel in the original image based on the multi-scale filtering denoising window is as follows: Figure 3 As shown, it may specifically include:

[0069] Step 301: Slide each filtering and denoising window in the pixel space provided by the original image.

[0070] Specifically, each filtering denoising window can be slid in the pixel space provided by the original image based on a preset sliding step size. The sliding step size adopted is preferably one pixel. Based on the sliding step size of one pixel, each pixel point in the original image can be processed as much as possible, thereby realizing traversal of each pixel point in the original image.

[0071] Taking a set of filter denoising windows including 3×3, 5×5, and 7×7 windows as an example, Figure 4(a)-Figure 4(c) Schematic diagrams of sliding each window in the pixel space of the original image are shown respectively.

[0072] Step 302: for each current position of the filtering and denoising window during the sliding process, pixel value processing is performed on each pixel point in the filtering and denoising window that slides to the current position in the original image to obtain a target pixel value, wherein the target pixel value is a denoising sub-result of the central pixel point in the filtering and denoising window at the current position.

[0073] Each time the filtering noise reduction window slides to a position during the sliding process, it is equivalent to traversing to the central pixel point of the window at the current position. Correspondingly, for the current position, the pixel point at the center of the filtering noise reduction window will be denoised.

[0074] Optionally, the pixel values ​​of each pixel point in the filtering and denoising window at the current position may be processed by smoothing filtering, median filtering or low-pass filtering included in spatial domain filtering. Taking smoothing filtering or median filtering as an example, the average value or median of the pixel values ​​of each pixel point in the filtering and denoising window at the current position may be calculated, and the obtained target pixel value such as the average value or median value may be used as the denoising sub-result corresponding to the central pixel point in the window, thereby realizing the denoising processing of the currently traversed pixel point (i.e., the central pixel point of the window at the current position).

[0075] For example, in Figure 4(a), for the current position O to which the 3×3 window slides (assuming that the current position is identified based on the current center pixel of the filter denoising window), the above processing can be performed on pixel O and its 8 neighboring pixels, i.e., a total of 9 pixels, such as calculating the average pixel value of each pixel based on smoothing filtering or calculating the median pixel value of each pixel based on median filtering, etc. The calculated average or median target pixel value is the denoising sub-result of pixel O corresponding to the 3×3 window. Similarly, in Figure 4(b) , 4(c) In the above example, the 25 or 49 pixels in the neighborhood of the pixel point O are processed as above, and the denoising sub-results of the pixel point O corresponding to the window 5×5 and 7×7 are obtained.

[0076] Thus, the denoising sub-results corresponding to different filtering denoising windows for the pixels in the original image include: target pixel values ​​corresponding to different filtering denoising windows obtained by performing the above-mentioned pixel value processing on different groups of pixel points corresponding to different filtering denoising windows centered on the pixel point; a group of pixel points corresponding to a filtering denoising window includes all pixel points in the original image that are in the filtering denoising window.

[0077] By sliding each window in the multi-scale filtering denoising window continuously in the pixel space corresponding to the original image, and performing the above-mentioned denoising process on the central pixel point of the window at the current position each time it slides to a position, it is possible to use the multi-scale filtering denoising window to perform ergodic denoising process on each pixel point in the original image, and obtain corresponding denoising sub-results corresponding to different filtering denoising windows for each pixel point in the original image.

[0078] In practical applications, when using filtering denoising windows of different scales to perform traversal denoising processing on each pixel in the original image, optionally, the above-mentioned traversal denoising process can be independently executed for filtering denoising windows of different scales, and different denoised sub-images of the original image corresponding to different windows are obtained accordingly. That is, for a number of n filtering denoising windows, the original image is traversed n times in total, and a total of n denoised sub-images of the original image corresponding to n filtering denoising windows are obtained. It is easy to understand that the denoised sub-image of the original image corresponding to each filtering denoising window is composed of the denoising sub-results of each pixel in the original image corresponding to the window.

[0079] As another optional implementation, when performing the above-mentioned traversal denoising process on the original image, each filtering denoising window can be synchronously slid on the original image, wherein each sliding moves each filtering denoising window from the previous same position to the next same position. For details, please refer to Figure 5 The schematic diagram of synchronously sliding each filtering and denoising window is shown, each filtering and denoising window has the same center point pixel, that is, each filtering and denoising window is considered to be in the same position, and whenever each filtering and denoising window is synchronously slid to a certain same position, the pixel point at the center of each window at that position (that is, the center pixel point) is respectively calculated based on the neighborhood space provided by each window to calculate different denoising sub-results of the center pixel point corresponding to different windows. In this implementation, a single traversal of the original image can obtain the denoising sub-results of each pixel point of the original image corresponding to different filtering and denoising windows.

[0080] The above two traversal methods both aim to obtain a plurality of denoising sub-results corresponding to different filtering denoising windows for each pixel point in the original image.

[0081] In practical applications, for pixels at the physical edge of an image, such as pixels at the upper, lower, left, and right edges of an image, when performing traversal denoising based on window sliding, these pixels at the physical edge of the image lack at least some of the neighboring pixels in a specific direction, resulting in an incomplete neighborhood space. Taking a 5×5 window as an example, the pixels in the left two columns, right two columns, upper two rows, and lower two rows of pixels in the image are pixels with incomplete neighborhood space. These pixels cannot be used as the central pixels of the neighborhood space, and accordingly, it is impossible to implement traversal for these pixels based on window sliding. For this situation, the missing pixels of the pixel in a certain direction can be supplemented by mirroring. Specifically, for a specific edge pixel X, if its field pixels in a certain direction are missing, the pixel values ​​of the reverse equidistant pixels with the pixel X as the symmetric center can be used to fill the missing pixel values. Afterwards, the above-mentioned denoising process for these pixels (physical edge pixels) can be further performed based on the complete neighborhood space formed after pixel supplementation.

[0082] Or, optionally, for the edge pixel points whose part of the neighborhood space is missing, the traversal of the part of the pixel points may be directly ignored, and this embodiment does not impose any limitation on this.

[0083] Step 103: According to the denoising sub-results of different filtering denoising windows corresponding to the pixels in the original image, the denoising results corresponding to the pixels are processed.

[0084] The noise reduction results corresponding to the pixels in the original image constitute the noise reduction image corresponding to the original image.

[0085] After the original image is denoised using multi-scale filtering and denoising windows respectively, and the denoising sub-results corresponding to the filtering and denoising windows of different scales are obtained for each pixel in the original image, the weights assigned to the different filtering and denoising windows are further obtained, and based on the weights assigned to the different filtering and denoising windows, the denoising sub-results of each pixel are weightedly calculated, and finally the denoising result of each pixel is obtained. The denoising results corresponding to each pixel constitute the denoised image corresponding to the original image.

[0086] In order to make the weight more reasonable, preferably, the weight corresponding to the filtering denoising window is negatively correlated with the scale of the filtering denoising window, that is, a larger weight is assigned to a smaller window, and a smaller weight is assigned to a larger window.

[0087] Combined with the different weights assigned to different filtering denoising windows, by performing weighted calculations on the different denoising sub-results corresponding to different filtering denoising windows for each pixel of the original image, the denoising results corresponding to each pixel in the original image can be obtained. The denoising results corresponding to each pixel ultimately constitute the denoised image corresponding to the original image.

[0088] Based on the above-mentioned weight distribution principle, this embodiment exemplarily provides a calculation formula for the noise reduction result corresponding to the pixel points in the original image, which is as follows:

[0089]

[0090] in:

[0091] V Indicates the noise reduction result corresponding to the pixel point; V1 , V2 ... Vn Respectively represent the denoising sub-results corresponding to different filtering denoising windows of the pixel points; r1 , r2 ... rn They respectively represent different window radii corresponding to different filtering and denoising windows. The window radius corresponding to the filtering and denoising window may specifically refer to the radius of the circumscribed circle or the inscribed circle of the filtering and denoising window. n represents the number of filtering and denoising windows.

[0092] They represent different weights corresponding to different filtering and denoising windows.

[0093] In actual implementation, the calculation formula for pixel noise reduction results can be flexibly set based on the above-mentioned weight distribution principle, and this embodiment does not limit this.

[0094] It is easy to understand that based on the above processing, filtering and denoising windows of different scales have contributed to the denoising process of the original image. Thus, it takes into account both the advantages of large-scale window denoising and the advantages of small-scale windows in protecting image information such as image edges, details, textures and other information.

[0095] When performing image denoising, the embodiment of the present application adopts a combination of filtering and denoising windows of multiple scales (sizes) to filter and denoise the original image. Based on this method, the distribution information of the original image under denoising windows of different scales can be obtained. A large-scale window can be combined to obtain a better denoising effect, and a small-scale window can be combined to keep the image information from being lost, especially avoiding the loss of high-frequency information such as the edge, details, and texture of the image, thereby taking into account both the denoising effect and information protection of the image, avoiding the over-smoothing problem caused by image denoising, and further improving the image quality after denoising.

[0096] Corresponding to the above-mentioned image noise reduction method, the present application embodiment also discloses an image noise reduction device, see Figure 6 The schematic diagram of the structure of the image noise reduction device is shown, and the device may include:

[0097] An acquisition unit 601 is used to acquire an original image to be processed;

[0098] The first processing unit 602 is used to perform denoising processing on the pixel points in the original image using multiple filtering denoising windows respectively, to obtain denoising sub-results corresponding to different filtering denoising windows for the pixel points; different filtering denoising windows correspond to different window scales;

[0099] The second processing unit 603 is used to process the denoising sub-results corresponding to different filtering denoising windows of the pixel points in the original image to obtain the denoising results corresponding to the pixel points;

[0100] The noise reduction results corresponding to the pixels in the original image constitute a noise reduction image of the original image.

[0101] In an optional implementation of the embodiment of the present application, the first processing unit 602 is specifically configured to:

[0102] Sliding each filtering and denoising window in the pixel space provided by the original image;

[0103] For each current position of the filtering and denoising window during the sliding process, pixel value processing is performed on each pixel point in the filtering and denoising window that slides to the current position in the original image to obtain a target pixel value;

[0104] Among them, the target pixel value is the denoising sub-result of the central pixel point in the filtering denoising window of the current position; the denoising sub-results of the pixel point corresponding to different filtering denoising windows include: the target pixel values ​​corresponding to different filtering denoising windows are obtained by performing the pixel value processing on different groups of pixel points corresponding to different filtering denoising windows centered on the pixel point; a group of pixel points corresponding to a filtering denoising window includes each pixel point in the original image that is in the filtering denoising window.

[0105] In an optional implementation of the embodiment of the present application, the second processing unit 603 is specifically configured to:

[0106] Get the weights assigned to different filtering and denoising windows respectively;

[0107] Based on the weights respectively assigned to different filtering denoising windows, different denoising sub-results corresponding to the pixel points are weightedly processed to obtain the denoising result corresponding to the pixel points.

[0108] In an optional implementation of the embodiment of the present application, the weight size corresponding to the filtering and denoising window is negatively correlated with the scale size of the filtering and denoising window.

[0109] In an optional implementation of the embodiment of the present application, the second processing unit 603 performs weighted processing on different denoising sub-results corresponding to the pixel point based on the weights respectively assigned to different filtering denoising windows to obtain the denoising result corresponding to the pixel point, including:

[0110] Use the following formula to calculate the noise reduction result corresponding to the pixel:

[0111]

[0112] in:

[0113] V Indicates the noise reduction result corresponding to the pixel point; V1 , V2 ... Vn They represent different denoising sub-results corresponding to different filtering denoising windows for the pixel points; r1 , r2 ... rn They represent different window radii corresponding to different filter denoising windows; n represents the number of filter denoising windows;

[0114] They represent different weights corresponding to different filtering and denoising windows.

[0115] As for the image denoising device disclosed in the embodiment of the present application, since it corresponds to the image denoising method disclosed in the above embodiment, the description is relatively simple. For the relevant similarities, please refer to the description of the image denoising method in the above embodiment, which will not be described in detail here.

[0116] An embodiment of the present application also discloses an electronic device, which may be, but is not limited to, a terminal device such as a mobile phone, a tablet computer, a personal PC (such as a notebook, an all-in-one computer, a desktop computer) with an image processing function, or a physical machine corresponding to a private cloud / public cloud platform, a server (image processing server), etc. with an image processing function.

[0117] The electronic device has a structure such as Figure 7 As shown, including at least:

[0118] Memory 701, used for storing computer instruction sets;

[0119] The computer instruction set may be implemented in the form of a computer program.

[0120] The memory 701 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0121] The processor 702 is used to implement the image denoising method disclosed in the above method embodiment by executing the instruction set stored in the memory.

[0122] The processor 702 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices.

[0123] In addition, the electronic device may also include components such as a communication interface and a communication bus. The memory, the processor and the communication interface communicate with each other through the communication bus.

[0124] The communication interface is used for communication between electronic devices and other devices. The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0125] In this embodiment, the processor in the electronic device executes the computer instruction set stored in the memory, and when performing image denoising, it comprehensively adopts filtering and denoising windows of multiple scales (sizes) to filter and denoise the original image. Based on this method, the distribution information of the original image under the denoising windows of different scales can be obtained, and a large-scale window can be combined to obtain a better denoising effect, and a small-scale window can be combined to keep the image information from being lost, especially avoiding the loss of high-frequency information such as the edge, details, and texture of the image, thereby taking into account both the denoising effect and information protection of the image, avoiding the over-smoothing problem caused by image denoising, and further improving the image quality after denoising.

[0126] In addition, an embodiment of the present application further discloses a computer-readable storage medium, which stores a computer instruction set. When the computer instruction set is executed by a processor, the image denoising method disclosed in the above method embodiment is implemented.

[0127] When the instructions stored in the computer-readable storage medium are run, a plurality of scales (sizes) of filtering and denoising windows are comprehensively adopted to filter and denoise the original image. Based on this method, the distribution information of the original image under different scales of denoising windows can be obtained, and a large-scale window can be combined to obtain a better denoising effect, and a small-scale window can be combined to keep the image information from being lost, especially avoiding the loss of high-frequency information such as the edge, details, and texture of the image, thereby taking into account both the denoising effect and information protection of the image, avoiding the over-smoothing problem caused by image denoising, and further improving the image quality after denoising.

[0128] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0129] For the convenience of description, the above system or device is described by dividing it into various modules or units according to its functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0130] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0131] Finally, it should be noted that, in this article, relational terms such as first, second, third and fourth are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0132] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An image denoising method, characterized in that: include: Obtain the original image to be processed; denoising the pixels in the original image using a plurality of filtering denoising windows respectively, to obtain denoising sub-results of the pixels corresponding to different filtering denoising windows respectively; different filtering denoising windows correspond to different window scales; According to the denoising sub-results of the pixel points in the original image corresponding to different filtering denoising windows, the denoising results corresponding to the pixel points are obtained by processing; The noise reduction results corresponding to the pixels in the original image constitute a noise reduction image of the original image. The processing of obtaining the denoising results corresponding to the pixels in the original image by processing the denoising sub-results of different filtering denoising windows respectively includes: Get the weights assigned to different filtering and denoising windows respectively; Based on the weights assigned to the different filtering denoising windows, weighted processing is performed on different denoising sub-results corresponding to the pixel point to obtain the denoising result corresponding to the pixel point; The weighted processing of different denoising sub-results corresponding to the pixel points based on the weights respectively assigned to different filtering denoising windows to obtain the denoising results corresponding to the pixel points includes: Use the following formula to calculate the noise reduction result corresponding to the pixel: in: V represents the noise reduction result corresponding to the pixel; V1, V2...V n They represent the different denoising results of the pixel points corresponding to different filtering denoising windows; r1, r2...r n They represent different window radii corresponding to different filter denoising windows; n represents the number of filter denoising windows; They represent different weights corresponding to different filtering and denoising windows.

2. The method according to claim 1, characterized in that The denoising process is performed on the pixels in the original image by using a plurality of filtering denoising windows respectively to obtain denoising sub-results of the pixels corresponding to different filtering denoising windows respectively, including: Sliding each filtering and denoising window in the pixel space provided by the original image; For each current position of the filtering and denoising window during the sliding process, pixel value processing is performed on each pixel point in the filtering and denoising window that slides to the current position in the original image to obtain a target pixel value; Among them, the target pixel value is the denoising sub-result of the central pixel point in the filtering denoising window of the current position; the denoising sub-results of the pixel point corresponding to different filtering denoising windows include: the target pixel values ​​corresponding to different filtering denoising windows are obtained by performing the pixel value processing on different groups of pixel points corresponding to different filtering denoising windows centered on the pixel point; a group of pixel points corresponding to a filtering denoising window includes each pixel point in the original image that is in the filtering denoising window.

3. The method according to claim 1, characterized in that The weight size corresponding to the filter denoising window is negatively correlated with the scale size of the filter denoising window.

4. An image noise reduction device, characterized in that: include: An acquisition unit, used for acquiring an original image to be processed; A first processing unit is used to perform denoising processing on the pixel points in the original image using a plurality of filtering denoising windows respectively, to obtain denoising sub-results corresponding to the pixel points in different filtering denoising windows respectively; different filtering denoising windows correspond to different window scales; A second processing unit is used to process the denoising sub-results corresponding to different filtering denoising windows of the pixel points in the original image to obtain the denoising results corresponding to the pixel points; The noise reduction results corresponding to the pixels in the original image constitute a noise reduction image of the original image. The second processing unit is specifically configured to: Get the weights assigned to different filtering and denoising windows respectively; Based on the weights assigned to the different filtering denoising windows, weighted processing is performed on different denoising sub-results corresponding to the pixel point to obtain the denoising result corresponding to the pixel point; The second processing unit performs weighted processing on different denoising sub-results corresponding to the pixel points based on the weights respectively assigned to different filtering denoising windows to obtain the denoising results corresponding to the pixel points, including: Use the following formula to calculate the noise reduction result corresponding to the pixel: in: V represents the noise reduction result corresponding to the pixel; V1, V2...V n They represent the different denoising results of the pixel points corresponding to different filtering denoising windows; r1, r2...r n They represent different window radii corresponding to different filter denoising windows; n represents the number of filter denoising windows; They represent different weights corresponding to different filtering and denoising windows.

5. The device according to claim 4, characterized in that The first processing unit is specifically configured to: Sliding each filtering and denoising window in the pixel space provided by the original image; For each current position of the filtering and denoising window during the sliding process, pixel value processing is performed on each pixel point in the filtering and denoising window that slides to the current position in the original image to obtain a target pixel value; Among them, the target pixel value is the denoising sub-result of the central pixel point in the filtering denoising window of the current position; the denoising sub-results of the pixel point corresponding to different filtering denoising windows include: the target pixel values ​​corresponding to different filtering denoising windows are obtained by performing the pixel value processing on different groups of pixel points corresponding to different filtering denoising windows centered on the pixel point; a group of pixel points corresponding to a filtering denoising window includes each pixel point in the original image that is in the filtering denoising window.

6. The device according to claim 4, characterized in that The weight size corresponding to the filter denoising window is negatively correlated with the scale size of the filter denoising window.

7. An electronic device, characterized in that: include: Memory, used to store computer instruction sets; A processor, configured to implement the image denoising method according to any one of claims 1 to 3 by executing an instruction set stored in the memory.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer instruction set, and when the computer instruction set is executed by a processor, the image denoising method according to any one of claims 1 to 3 is implemented.

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