WO2017101489A1

WO2017101489A1 - Method and device for image filtering

Info

Publication number: WO2017101489A1
Application number: PCT/CN2016/096009
Authority: WO
Inventors: 边智; 魏伟; 白茂生; 刘阳
Original assignee: 乐视控股（北京）有限公司; 乐视云计算有限公司
Priority date: 2015-12-14
Filing date: 2016-08-19
Publication date: 2017-06-22
Also published as: CN105894460A

Abstract

Provided are a method and device for image filtering. The method comprises: superimposing each frame of image to be processed with multiple frames of consecutive images preceding the image to be processed, thus acquiring a fused image (101); with respect to each pixel in the fused image, calculating the average of pixel values of other pixels excluding the pixel in a first surrounding area of the pixel (102); when the difference between the pixel value of the pixel and the average of the pixel values corresponding thereto exceeds a preset range, updating the pixel value of the pixel to the average of the pixel values corresponding thereto, thus removing a low frequency noise (103); and spatially filtering the fused image having low frequency noises removed, thus removing high frequency noises, and acquiring a filtered image of the image to be processed (104). The present invention effectively implements image denoise filtering.

Description

Image filtering method and device

The present application claims priority to Chinese Patent Application No. 2015109261425, the entire disclosure of which is incorporated herein by reference.

Technical field

The embodiments of the present invention relate to the field of video technologies, and in particular, to an image filtering method and apparatus.

Background technique

Image filtering means that the noise of the image is suppressed under the condition that the image details are preserved, which is an important means to reduce image noise and enhance image quality.

Since the video may be interfered by noise during the process of acquisition, transmission, etc., the image quality is degraded. When the video is encoded, the coding performance is degraded. Therefore, how to effectively remove the image noise in the video becomes a technology in the field. The technical problems that personnel urgently need to solve.

Summary of the invention

The embodiment of the present application provides an image filtering method and device, which are used to effectively remove image noise.

An embodiment of the present application provides an image filtering method, including:

And superimposing each frame of the image to be processed and the continuous multi-frame image before the image to be processed to obtain a fused image; the pixel value of each pixel in the fused image is the image to be processed and the to-be-processed The average of the pixel values of the pixel at the same position in consecutive multi-frame images before the image;

Calculating, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point;

When the difference between the pixel value of the pixel and the corresponding pixel value average value exceeds a preset range, the pixel value of the pixel point is updated to its corresponding pixel value average value to remove low frequency noise;

The fused image from which the low frequency noise is removed is subjected to spatial filtering to remove high frequency noise, and a filtered image of the image to be processed is obtained.

An embodiment of the present application provides an image filtering apparatus, including:

An image superimposing module, configured to superimpose each frame of the image to be processed and the continuous multi-frame image before the image to be processed to obtain a fused image; the pixel value of each pixel in the fused image is the to-be-processed An average of pixel values of the same position in the image and the consecutive multi-frame images preceding the image to be processed;

a calculation module, configured to calculate, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point;

a first filtering module, configured to: when a difference between a pixel value of the pixel point and a corresponding pixel value average thereof exceeds a preset range, update a pixel value of the pixel point to an average value of the corresponding pixel value, and remove Low frequency noise

And a second filtering module, configured to perform spatial filtering on the fused image that removes low frequency noise, remove high frequency noise, and obtain a filtered image of the image to be processed.

The embodiment of the present application provides an electronic device, including: the above image filtering device.

The embodiment of the present application provides a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium can store a computer program, and the computer program can implement a part of the image filtering method described above when executed. Or all steps.

An embodiment of the present application further provides an electronic device, including: one or more processors; and a memory; wherein the memory stores instructions executable by the one or more processors, the instructions being The one or more processors are executed to enable the one or more processors to perform the image filtering method of any of the above-described embodiments of the present application.

An embodiment of the present application provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer, The computer is caused to perform the image filtering method of any of the above embodiments of the present application.

The image filtering method and apparatus provided by the embodiments of the present application obtains a fused image by accumulating each frame of the image to be processed, and a plurality of consecutive frames thereof, and calculating the fused image for each pixel in the fused image. An average value of pixel values of other pixel points of the pixel point is not included in the first surrounding area of the pixel; when the difference between the pixel value of the pixel point and the corresponding average value of the pixel value exceeds a preset range, The pixel value of the pixel is updated to its corresponding pixel value average value to remove low frequency noise; the fused image from which low frequency noise is removed is spatially filtered to remove high frequency noise, that is, the image of the image to be processed can be obtained. The image effectively removes low frequency noise and high frequency noise of the image, which improves the effectiveness of image filtering.

DRAWINGS

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description of the drawings used in the embodiments or the prior art description will be briefly described below. Obviously, the drawings in the following description It is a certain embodiment of the present application, and other drawings can be obtained according to the drawings without any creative work for those skilled in the art.

1 is a flow chart of an embodiment of an image filtering method of the present application;

2 is a flow chart of still another embodiment of an image filtering method of the present application;

3 is a schematic structural diagram of an embodiment of an image filtering device of the present application;

4 is a schematic structural diagram of still another embodiment of an image filtering apparatus according to the present application;

FIG. 5 is a schematic diagram of a hardware structure of an apparatus for an image filtering method according to an embodiment of the present application.

detailed description

The technical solutions in the embodiments of the present application are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present application. It is a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

At present, the image sensor market mainly has CMOS (Complementary Metal Oxide Semiconductor, Complementary metal oxide semiconductor) image sensor and CCD (Charge-coupled Device) image sensor. Due to the increased consumption of miniaturized, low-power, and low-cost imaging systems, cameras often use CMOS image sensors.

The inventors found in the research that CMOS image sensors have serious noise problems due to process and technical reasons. Especially in the dark light conditions, the noise problem of CMOS image sensors is more prominent, which is related to the device and the process itself. Therefore, image noise is mainly caused by chip noise, so it is necessary to remove chip noise in the image.

The chip noise in the image mainly includes low frequency noise high frequency noise. The inventor further found in the research that the low-frequency noise of the chip is caused by the instability of the additive in the chip manufacturing process, and the expression form is that the acquired signal value is long or long, and is difficult to detect in a single image. Therefore, it can be After multiple images are superimposed, they are detected. For high-frequency noise in the image, spatial domain information can be utilized to filter and eliminate high-frequency noise of the chip. The high-frequency noise of the chip appears as a random signal, so it can be processed by the spatial domain information to smooth the image.

In summary, the inventor has proposed a technical solution of the present application through a series of studies. In the embodiment of the present application, for each image to be processed in the video, a continuous multi-frame image before each frame of the image to be processed may be selected and performed. Accumulating, obtaining a fused image for detecting low frequency noise; for each pixel in the fused image, calculating pixel values of other respective pixel points not including the pixel in the first surrounding area of the pixel Average value; when the difference between the pixel value of the pixel point and the corresponding pixel value average exceeds a preset range, the pixel value of the pixel point is updated to its corresponding pixel value average value to remove low frequency noise; The fused image of the low-frequency noise is removed and the spatial filtering is performed to remove the high-frequency noise, so that the filtered image of the image to be processed is obtained. In the embodiment of the present application, the low-frequency noise and high-frequency noise of the image are effectively removed, and the image filtering is improved. Effectiveness. The algorithm has high performance and fast speed, which improves the efficiency of image filtering.

FIG. 1 is a flowchart of an embodiment of an image filtering method according to an embodiment of the present disclosure, where Including the following steps:

101: Superimpose each frame of the image to be processed and the continuous multi-frame image before the image to be processed to obtain a fused image.

The image to be processed refers to an image that needs to be denoised and filtered for each frame in the video.

Each frame of the image to be processed and the first few frames of the image to be processed of each frame may be superimposed, and by superimposing the plurality of images, the low frequency noise in the image to be processed may be detected.

Wherein, the pixel pixel value of each position in the obtained fused image is an average value of the pixel value of the pixel at the same position in the image to be processed and the consecutive multi-frame image before the image to be processed.

For example, each frame of the image to be processed and the continuous multi-frame image before each frame of the image to be processed assume a total of N frames of continuous images, and N frames of consecutive images are superimposed to obtain a fused image, and the pixel value of each pixel is N. The pixel values of the pixels at the same position in the continuous image of the frame are accumulated and divided by N.

102: Calculate, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point.

103: When the difference between the pixel value of the pixel point and the corresponding pixel value average value exceeds a preset range, update the pixel value of the pixel point to its corresponding pixel value average value to remove low frequency noise.

The first surrounding area refers to a first predetermined range centered on the pixel. For example, centered on each pixel, the first surrounding area may be an area within 5*5 around the pixel.

Therefore, for each pixel point, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point is calculated, and the pixel value of the other each pixel point may be replaced by the pixel value of the pixel point. .

Specifically, when the difference between the pixel value of the pixel point and the average value of the corresponding pixel value exceeds a preset range, it indicates that the image is abruptly changed, which may be considered to be caused by low frequency noise, so that the pixel point is The pixel value is updated to its corresponding pixel value average.

Here, the average value of the pixel values corresponding to the pixel points is the average value of the pixel values of the other respective pixel points in the first surrounding area of the pixel point calculated in step 102.

104: Perform spatial filtering on the fused image from which low frequency noise is removed, remove high frequency noise, and obtain a filtered image of the image to be processed.

The fused image of the low frequency noise is removed, and the spatial domain filtering is performed by using the spatial domain information, that is, the high frequency noise can be removed, thereby obtaining the filtered image of the image to be processed.

In the embodiment of the present application, for each frame to be processed, a multi-frame image may be selected and accumulated to perform low-frequency noise detection; for each pixel in the fused image, the first pixel point is calculated. An average value of pixel values of other pixel points of the pixel is not included in the surrounding area; when the difference between the pixel value of the pixel and the corresponding pixel value average exceeds a preset range, the pixel point is The pixel value is updated to the corresponding pixel value average value to remove the low frequency noise; the fused image from which the low frequency noise is removed is spatially filtered, and the high frequency noise is removed, that is, the filtered image of the image to be processed can be obtained, and the image is effectively removed. Low frequency noise and high frequency noise of the image.

The difference between the pixel value of the pixel and the corresponding pixel value average value exceeds a preset range, and the method may include:

The pixel value of the pixel is greater than the corresponding pixel value average, and the difference is greater than the first preset value, or less than the corresponding pixel value average value, and the difference is less than the second preset value.

Therefore, the difference between the pixel value of the pixel and the corresponding pixel value average exceeds a preset range, and the pixel value of the pixel is updated to its corresponding pixel value average value, and removing the low frequency noise may include:

When the pixel value of the pixel is greater than the corresponding pixel value average and the difference is greater than the first preset value, or is less than the corresponding pixel value average value and the difference is less than the second preset value, the pixel is The pixel value of the point is updated to its corresponding pixel value average to remove low frequency noise.

As a possible implementation manner, the first preset value and the second preset value may be 30% of an average value of pixel values corresponding to the pixel points.

When the difference exceeds the error preset range, it can be determined that the image is abruptly changed and can be considered as a result of low frequency noise. Therefore, the pixel points of the pixel points can be replaced by the average of the pixel values of the other pixel points in the first surrounding area. If the difference is within the error preset range, the pixel value of the pixel point does not change, thereby being removed. A fused image of low frequency noise.

The fused image from which the low frequency noise is removed is subjected to spatial filtering to remove the high frequency noise, and the filtered image of the image to be processed is obtained by using the spatial information of each pixel in the fused image from which the low frequency noise is removed. Filtering to remove high frequency noise, specifically, as described in the embodiment shown in FIG.

FIG. 2 is a flowchart of still another embodiment of an image filtering method according to an embodiment of the present disclosure, where the method may include the following steps:

201: Superimpose each frame of the image to be processed and the continuous multi-frame image before the image to be processed to obtain a fused image.

The pixel pixel value of each position in the fused image is an average value of the pixel value of the pixel at the same position in the image to be processed and the continuous multi-frame image before the image to be processed.

202: Calculate, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point.

203: When the difference between the pixel value of the pixel and the corresponding pixel value average exceeds a preset range, update the pixel value of the pixel point to its corresponding pixel value average value to remove low frequency noise.

The operations in steps 201 to 203 are the same as the operations in steps 101 to 103 in the embodiment shown in FIG.

204: calculating, for each pixel point in the fused image that removes low frequency noise, calculating the pixel point The weighting factors of the other respective pixel points of the pixel are not included in the second surrounding area.

The second surrounding area may refer to a second predetermined range centered on the pixel.

High frequency noise can be removed from the fused image by utilizing the correlation of the pixel points with other individual pixels in the second surrounding area.

First, the weighting factors of other individual pixel points in the second surrounding area are calculated.

205: Calculate a weighted average of the pixel points by using a weighting factor of each of the other pixel points in the second surrounding area and a pixel value of the pixel point.

The pixel values of the pixel points are respectively multiplied by the weighting factors of the other respective pixel points, and the obtained products are accumulated, that is, a weighted average of the pixel points can be obtained.

206: Replace the pixel value of the pixel point with the weighted average value to remove high frequency noise.

207: Obtain a filtered image of the image to be processed.

The pixel value of the pixel is replaced with a weighted average of the pixel, that is, the high frequency noise of the fused image can be removed, so that the final filtered image can be obtained.

The weighting factor of the pixel values of the other surrounding pixels that do not include the pixel points in the second surrounding area of the pixel may be calculated in multiple ways.

As a possible implementation manner, the pixel in the second surrounding area of the pixel point that does not include the other pixel points of the pixel point is calculated for each pixel point in the fused image from which low frequency noise is removed. The weighting factor for the value can be:

For each pixel point in the fused image from which low frequency noise is removed, a weighting factor of each of the other pixel points in the second surrounding area of the pixel point is calculated according to a weighting factor calculation formula;

Where W(k,l) is the weighting factor of any one of the other pixel points (k,l) in the second surrounding area, 0≤W(k,l)≤1, ∑W(k,l) =1, T is the number of pixels in the second surrounding area; D(k, l) is the pixel value of the pixel (k, l) of the second surrounding area.

The weighted average of the pixel points may be specifically calculated according to a weighted average calculation formula, and thus the weight factor of the pixel values of the other respective pixel points in the second surrounding area and the pixel value of the pixel point are calculated. The weighted average of the pixels, removing high frequency noise can include:

Calculating, by using a weighting average of the pixel values of the other respective pixel points in the second surrounding area and pixel values of the pixel points, a weighted average of the pixel points according to a weighted average calculation formula;

v(i,j) represents any pixel point, and u(i,j) represents a weighted average of the pixel points.

The pixel point pixel value is replaced with a weighted average of the pixel points, that is, the high frequency noise can be removed, so that the filtered image of the image to be processed can be obtained.

In the embodiment of the present application, image filtering can be effectively implemented, so that low frequency noise and high frequency noise of the image can be effectively removed. The algorithm has high performance and fast speed, which improves the efficiency of image filtering.

FIG. 3 is a schematic structural diagram of an embodiment of an image filtering apparatus according to an embodiment of the present disclosure, where the apparatus may include:

The image superimposing module 301 is configured to superimpose each frame to be processed image and consecutive multi-frame images before the image to be processed to obtain a fused image.

By superimposing multiple images, low frequency noise in the image to be processed can be detected.

The pixel pixel value of each position in the fused image is an average value of pixel values of the pixel at the same position in the image to be processed and the continuous multi-frame image before the image to be processed.

The calculating module 302 is configured to calculate, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point.

The first filtering module 303 is configured to update the pixel value of the pixel point to an average value of the corresponding pixel value when the difference between the pixel value of the pixel point and the corresponding pixel value average value exceeds a preset range. Remove low frequency noise.

The first surrounding area refers to a first predetermined range centered on the pixel.

When the difference between the pixel value of the pixel and the corresponding pixel value average exceeds the preset range, it indicates that the image is abruptly changed, which can be considered as the occurrence of low frequency noise, so that the pixel value of the pixel is updated to The corresponding pixel value average.

The second filtering module 304 is configured to perform spatial filtering on the fused image from which low frequency noise is removed, remove high frequency noise, and obtain a filtered image of the image to be processed.

In the embodiment of the present application, for each frame to be processed, a multi-frame image may be selected and accumulated to perform low-frequency noise detection; for each pixel in the fused image, the first pixel point is calculated. An average value of pixel values of other pixel points of the pixel is not included in the surrounding area; when the difference between the pixel value of the pixel and the corresponding pixel value average exceeds a preset range, the pixel point is The pixel value is updated to its corresponding pixel value average value to remove low frequency noise; the fused image from which low frequency noise is removed is further subjected to airspace Filtering, removing high frequency noise, that is, obtaining a filtered image of the image to be processed, effectively removing low frequency noise and high frequency noise of the image.

Therefore, the first filtering module 303 can be specifically configured to:

The fused image from which the low frequency noise is removed is subjected to spatial filtering to remove the high frequency noise, and the filtered image of the image to be processed is obtained by using the spatial information of each pixel in the fused image from which the low frequency noise is removed. Filtering to remove the high-frequency noise. Specifically, as shown in FIG. 4, as a further embodiment, the second filtering module 304 may include:

a first calculating unit 401, configured to calculate, for each pixel point in the fused image from which low frequency noise is removed, a pixel value of a second surrounding area of the pixel point that does not include other pixel points of the pixel point Weight factor.

The second calculating unit 402 is configured to calculate a weighted average value of the pixel points by using a weighting factor of pixel values of other respective pixel points in the second surrounding area and a pixel value of the pixel point.

The filtering unit 403 is configured to replace the pixel value of the pixel point with the weighted average value, remove high frequency noise, and obtain a filtered image of the image to be processed.

As a possible implementation manner, the first calculating unit 401 may be specifically configured to:

Where W(k, l) is the weight of any one of the other pixels in the second surrounding area (k, l) Factor, 0 ≤ W (k, l) ≤ 1, ∑ W (k, l) = 1, T is the number of pixels in the second surrounding area; D (k, l) is the second surrounding area pixel ( The pixel value of k, l).

The weighted average of the pixel points can be calculated according to the weighted average calculation formula. As a further embodiment, the second calculating unit 402 can be specifically used for:

Calculating a weighted average of the pixel points according to a weighted average calculation formula as follows by using a weighting factor of pixel values of other respective pixel points in the second surrounding area and pixel values of the pixel points;

v(i,j) represents any pixel point, and u(i,j) represents a weighted average corresponding to the pixel points.

With the technical solution of the embodiment of the present application, image filtering can be effectively implemented, so that low frequency noise and high frequency noise of the image can be effectively removed. The algorithm has high performance and fast speed, which improves the efficiency of image filtering.

The embodiment of the present application further provides a non-transitory computer readable storage medium, where the non-transitory computer readable storage medium can store a program, and when the program is executed, the image filtering provided by any one of the foregoing embodiments can be implemented. Some or all of the steps in the various implementations of the method.

FIG. 5 is a schematic diagram of a hardware structure of an electronic device according to an image filtering method according to an embodiment of the present disclosure. As shown in FIG. 5, the device includes:

One or more processors 510 and memory 520, one processor 510 is taken as an example in FIG.

The apparatus for performing the image filtering method may further include: an input device 530 and an output device 540.

The processor 510, the memory 520, the input device 530, and the output device 540 may be connected by a bus or other means, as exemplified by a bus connection in FIG.

The memory 520 is a non-transitory computer readable storage medium and can be used to store non-volatile software programs. The program, the non-volatile computer executable program and the module, such as the program instruction/module corresponding to the image filtering method in the embodiment of the present application. The processor 510 executes various functional applications of the server and data processing by executing non-volatile software programs, instructions, and modules stored in the memory 520, that is, implementing the image filtering method of the above method embodiments.

The memory 520 may include a storage program area and an storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to use of the image filtering device, and the like. Further, the memory 520 may include a high speed random access memory, and may also include a nonvolatile memory such as at least one magnetic disk storage device, flash memory device, or other nonvolatile solid state storage device. In some embodiments, memory 520 can optionally include memory remotely disposed relative to processor 510, which can be coupled to the image filtering device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

Input device 530 can receive input digital or character information and generate key signal inputs related to user settings and function control of the image filtering device. The output device 540 can include a display device such as a display screen.

The one or more modules are stored in the memory 520, and when executed by the one or more processors 510, perform an image filtering method in any of the above method embodiments.

The above products can perform the methods provided by the embodiments of the present application, and have the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiments of the present application.

The electronic device of the embodiment of the invention exists in various forms, including but not limited to:

(1) Mobile communication devices: These devices are characterized by mobile communication functions and are mainly aimed at providing voice and data communication. Such terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

(2) Ultra-mobile personal computer equipment: This type of equipment belongs to the category of personal computers, has computing and processing functions, and generally has mobile Internet access. Such terminals include: PDAs, MIDs, and UMPC devices, such as the iPad.

(3) Portable entertainment devices: These devices can display and play multimedia content. Such devices include: audio, preview players (such as iPod), handheld game consoles, e-books, and smart toys and portable car navigation devices.

(4) Server: A device that provides computing services. The composition of the server includes a processor, a hard disk, a memory, and a system. The bus, etc., is similar to a general-purpose computer architecture, but requires high processing power, stability, reliability, security, scalability, manageability, and the like.

(5) Other electronic devices with data interaction functions.

The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment. Those of ordinary skill in the art can understand and implement without deliberate labor.

Through the description of the above embodiments, those skilled in the art can clearly understand that the various embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on such understanding, the above-described technical solutions may be embodied in the form of software products in essence or in the form of software products, which may be stored in a computer readable storage medium such as ROM/RAM, magnetic Discs, optical discs, etc., include instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) to perform the methods described in various embodiments or portions of the embodiments.

Finally, it should be noted that the above embodiments are only used to explain the technical solutions of the present application, and are not limited thereto; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still The technical solutions described in the foregoing embodiments are modified, or the equivalents of the technical features are replaced by the equivalents. The modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

An image filtering method, comprising:

And superimposing each frame of the image to be processed and the continuous multi-frame image before the image to be processed to obtain a fused image; the pixel value of each pixel in the fused image is the image to be processed and the to-be-processed The average of the pixel values of the pixel at the same position in consecutive multi-frame images before the image;

Calculating, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point;

When the difference between the pixel value of the pixel and the corresponding pixel value average value exceeds a preset range, the pixel value of the pixel point is updated to its corresponding pixel value average value to remove low frequency noise;

The fused image from which the low frequency noise is removed is subjected to spatial filtering to remove high frequency noise, and a filtered image of the image to be processed is obtained.
The method according to claim 1, wherein the fused image of the low frequency noise is subjected to spatial filtering to remove high frequency noise, and obtaining the filtered image of the image to be processed includes:

Calculating, for each pixel point in the fused image from which low frequency noise is removed, a weighting factor of pixel values of other respective pixel points not including the pixel point in the second surrounding area of the pixel point;

Calculating a weighted average of the pixel points by using a weighting factor of pixel values of other respective pixel points in the second surrounding area and pixel values of the pixel points, and replacing pixel values of the pixel points with the Weighted average to remove high frequency noise;

A filtered image of the image to be processed is obtained.
The method according to claim 2, wherein said calculating, for each pixel point in said fused image from which low frequency noise is removed, calculating other pixels in said second surrounding area of said pixel point that do not include said pixel point The weighting factors of the pixel values of the respective pixels include:

For each pixel in the fused image from which low frequency noise is removed, the weighting factor is as follows Calculating a weighting factor of each of the other pixels in the second surrounding area of the pixel;

Where W(k,l) is the weighting factor of the pixel point (k,l), 0≤W(k,l)≤1, ∑W(k,l)=1, and T is the other in the second surrounding area. The number of pixels; D(k, l) is the pixel value of the pixel (k, l) of the second surrounding area;

Calculating the weighted average of the pixel points by using a weighting factor of pixel values of other respective pixel points in the second surrounding area and pixel values of the pixel points includes:

Calculating a weighted average of the pixel points according to a weighted average calculation formula as follows by using a weighting factor of pixel values of other respective pixel points in the second surrounding area and pixel values of the pixel points;

v(i,j) represents any pixel point, and u(i,j) represents a weighted average corresponding to the pixel points.
The method according to claim 1, wherein the difference between the pixel value of the pixel point and the corresponding pixel value average exceeds a preset range, and the pixel value of the pixel point is updated to its corresponding pixel. The average value of the values to remove low frequency noise includes:

When the pixel value of the pixel is greater than the corresponding pixel value average and the difference is greater than the first preset value, or is less than the corresponding pixel value average value and the difference is less than the second preset value, the pixel is The pixel value of the point is updated to its corresponding pixel value average to remove low frequency noise.
The method according to claim 4, wherein the first preset value and the second preset value are 30% of an average value of pixel values corresponding to the pixel points.
An image filtering device, comprising:

An image superimposing module, configured to superimpose each frame of the image to be processed and the continuous multi-frame image before the image to be processed to obtain a fused image; the pixel value of each pixel in the fused image is An average of pixel pixel values of the same position in the continuous image and the continuous multi-frame image before the image to be processed;

a calculation module, configured to calculate, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point;

a first filtering module, configured to: when a difference between a pixel value of the pixel point and a corresponding pixel value average thereof exceeds a preset range, update a pixel value of the pixel point to an average value of the corresponding pixel value, and remove Low frequency noise

And a second filtering module, configured to perform spatial filtering on the fused image that removes low frequency noise, remove high frequency noise, and obtain a filtered image of the image to be processed.
The apparatus according to claim 6, wherein the second filtering module comprises:

a first calculating unit, configured to calculate, for each pixel point in the fused image from which the low frequency noise is removed, a weight of a pixel value of the other respective pixel points not including the pixel point in the second surrounding area of the pixel point factor;

a second calculating unit, configured to calculate a weighted average value of the pixel points by using a weighting factor of pixel values of other respective pixel points in the second surrounding area and a pixel value of the pixel point;

And a filtering unit, configured to replace the pixel value of the pixel point with the weighted average value, remove high frequency noise, and obtain a filtered image of the image to be processed.
The device according to claim 7, wherein the first calculating unit is specifically configured to:

For each pixel point in the fused image from which low frequency noise is removed, a weighting factor of each of the other pixel points in the second surrounding area of the pixel point is calculated according to a weighting factor calculation formula;

Where W(k,l) is the weighting factor of the pixel point (k,l), 0≤W(k,l)≤1, ∑W(k,l)=1, T is the first The number of other pixels in the surrounding area; D(k, l) is the pixel value of the pixel (k, l) of the second surrounding area;

The second calculating unit is specifically configured to:

Calculating a weighted average of the pixel points according to a weighted average calculation formula as follows by using a weighting factor of pixel values of other respective pixel points in the second surrounding area and pixel values of the pixel points;

v(i,j) represents any pixel point, and u(i,j) represents a weighted average corresponding to the pixel points.
The device according to claim 6, wherein the first filtering module is specifically configured to:

When the pixel value of the pixel is greater than the corresponding pixel value average and the difference is greater than the first preset value, or is less than the corresponding pixel value average value and the difference is less than the second preset value, the pixel is The pixel value of the point is updated to its corresponding pixel value average to remove low frequency noise.
The device according to claim 9, wherein the first preset value and the second preset value are 30% of an average value of pixel values corresponding to the pixel points.
A non-transitory computer readable storage medium, characterized in that the non-transitory computer readable storage medium stores a computer program for causing the computer to perform the method of any of claims 1-5 .
An electronic device comprising:

One or more processors; and,

Memory; characterized in that

The memory stores instructions executable by the one or more processors, the instructions being executed by the one or more processors to cause the one or more processors to:

And superimposing each frame of the image to be processed and the continuous multi-frame image before the image to be processed to obtain a fused image; the pixel value of each pixel in the fused image is the image to be processed and the to-be-processed The average of the pixel values of the pixel at the same position in consecutive multi-frame images before the image;

Calculating, for each pixel point in the fused image, an average value of pixel values of other respective pixel points not including the pixel point in the first surrounding area of the pixel point;

When the difference between the pixel value of the pixel and the corresponding pixel value average value exceeds a preset range, the pixel value of the pixel point is updated to its corresponding pixel value average value to remove low frequency noise;

The fused image from which the low frequency noise is removed is subjected to spatial filtering to remove high frequency noise, and a filtered image of the image to be processed is obtained.
A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to execute The method of claims 1-5.