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

Abstract

The invention relates to the technical field of image processing, and provides an image noise reduction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed single-frame image; performing edge detection on the single-frame image, and determining a weak edge region and a strong edge region of the single-frame image; performing noise reduction processing on the weak edge region based on a preset weak edge noise reduction algorithm to obtain a weak edge noise reduction result; performing noise reduction processing on the strong edge region based on a noise reduction region and a comparison region determined by the strong edge region to obtain a strong edge noise reduction result; and based on the weak edge noise reduction result and the strong edge noise reduction result, determining a noise reduction image corresponding to the single-frame image. By adopting the scheme of the invention, the image quality can be ensured while the noise reduction efficiency is improved.

Description

Technical Field

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

[0002] Noise is an important factor affecting image quality, so it is necessary to perform noise reduction on images to ensure image quality.

[0003] At present, the commonly used noise reduction methods can be divided into two categories: single-frame noise reduction and multi-frame noise reduction. Among them, single-frame noise reduction is to use common noise reduction methods such as median filtering to perform overall noise reduction on the image. The noise reduction effect of this noise reduction method is not good and it is difficult to ensure image quality; multi-frame noise reduction is to shoot the same scene multiple times and merge the multiple images taken together through an algorithm to achieve the purpose of noise reduction. This noise reduction method takes a long time to process multiple images and has low noise reduction efficiency. Summary of the invention

[0004] Based on this, it is necessary to provide an image noise reduction method, device, electronic device and storage medium that can improve the noise reduction efficiency while ensuring the noise reduction effect in response to the above technical problems.

[0005] The present disclosure provides an image noise reduction method, the method comprising:

[0006] Obtain a single frame image to be processed;

[0007] Performing edge detection on the single-frame image to determine a weak edge region and a strong edge region of the single-frame image;

[0008] Performing denoising processing on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result;

[0009] Performing noise reduction processing on the strong edge area based on the noise reduction area and the comparison area determined in the strong edge area to obtain a strong edge noise reduction result;

[0010] Based on the weak edge denoising result and the strong edge denoising result, a denoised image corresponding to the single frame image is determined.

[0011] In one embodiment, performing edge detection on the single frame image to determine the weak edge area and the strong edge area of ​​the single frame image includes:

[0012] Performing grayscale processing on the single-frame image to obtain a grayscale image;

[0013] Performing edge detection on the grayscale image based on a preset edge detection algorithm to determine an edge detection result, wherein the edge detection result includes the coordinates of the edge points and the gradient values ​​corresponding to the edge points;

[0014] Determine weak edge points and strong edge points based on the gradient value of each edge point in the edge detection result and a preset gradient threshold;

[0015] A weak edge region of the single frame image is determined based on the coordinates of the weak edge points, and a strong edge region of the single frame image is determined based on the coordinates of the strong edge points.

[0016] In one embodiment, the noise reduction area and the contrast area determined based on the strong edge area are subjected to noise reduction processing on the strong edge area to obtain a strong edge noise reduction result, including:

[0017] Determine a plurality of pixel points from the strong edge region;

[0018] Traversing each pixel point among the plurality of pixel points, taking the currently traversed pixel point as the center point, and determining the noise reduction area from the strong edge area according to a preset length and a preset width;

[0019] Performing noise reduction processing on the noise reduction area based on a plurality of comparison areas, wherein each comparison area is determined from the strong edge area according to the preset length and the preset width, and the currently traversed pixel point is not included in the comparison area;

[0020] After traversing the plurality of pixel points, the strong edge denoising result is obtained.

[0021] In one embodiment, the performing noise reduction processing on the noise reduction area based on multiple comparison areas includes:

[0022] Determine a first preset number of contrast regions from the strong edge region, wherein the first preset number is greater than 1;

[0023] Calculating the similarity between each of the comparison areas and the noise reduction area;

[0024] According to the similarity, determining a target comparison area having a similarity greater than a preset value from the first preset number of comparison areas;

[0025] The noise reduction area is subjected to noise reduction processing based on the target comparison area.

[0026] In one embodiment, the performing noise reduction processing on the noise reduction area based on multiple comparison areas includes:

[0027] Determine a current contrast region from the strong edge region;

[0028] Calculating the similarity between the current comparison area and the noise reduction area;

[0029] Counting the number of target comparison areas whose similarities in the determined comparison areas are greater than a similarity threshold;

[0030] When the number of the target comparison areas reaches a second preset number, noise reduction processing is performed on the noise reduction area based on the target comparison areas.

[0031] The present disclosure also provides an image noise reduction device, the device comprising:

[0032] An image acquisition module, used for acquiring a single frame image to be processed;

[0033] An edge detection module, used to perform edge detection on the single frame image to determine a weak edge region and a strong edge region of the single frame image;

[0034] A weak edge denoising module, used to perform denoising processing on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result;

[0035] A strong edge denoising module, used for performing denoising processing on the strong edge area based on the denoising area and the contrast area determined in the strong edge area, to obtain a strong edge denoising result;

[0036] A result fusion module is used to determine a denoised image corresponding to the single frame image based on the weak edge denoising result and the strong edge denoising result.

[0037] In one embodiment, the edge detection module is further used to:

[0038] Performing grayscale processing on the single-frame image to obtain a grayscale image;

[0039] Performing edge detection on the grayscale image based on a preset edge detection algorithm to determine an edge detection result, wherein the edge detection result includes the coordinates of the edge points and the gradient values ​​corresponding to the edge points;

[0040] Determine weak edge points and strong edge points based on the gradient value of each edge point in the edge detection result and a preset gradient threshold;

[0041] A weak edge region of the single frame image is determined based on the coordinates of the weak edge points, and a strong edge region of the single frame image is determined based on the coordinates of the strong edge points.

[0042] In one embodiment, the strong edge denoising module comprises:

[0043] A first determining unit, configured to determine a plurality of pixel points from the strong edge region;

[0044] A second determining unit is used to traverse each pixel point of the plurality of pixel points, take the currently traversed pixel point as the center point, and determine the noise reduction area from the strong edge area according to a preset length and a preset width;

[0045] a noise reduction unit, configured to perform noise reduction processing on the noise reduction area based on a plurality of comparison areas, wherein each comparison area is determined from the strong edge area according to the preset length and the preset width, and the comparison area does not contain the currently traversed pixel point;

[0046] The result determination unit is used to obtain the strong edge denoising result after traversing the multiple pixel points.

[0047] An embodiment of the present disclosure further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the image noise reduction method provided in any embodiment of the present disclosure are implemented.

[0048] An embodiment of the present disclosure provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the image denoising method provided by any embodiment of the present disclosure are implemented.

[0049] An embodiment of the present disclosure provides a computer program product, which is used to execute the steps of the image denoising method provided by any embodiment of the present disclosure.

[0050] The image denoising method, device, electronic device and storage medium provided by the embodiments of the present disclosure obtain a single-frame image to be processed, perform edge detection on the single-frame image, determine the weak edge area and the strong edge area of ​​the single-frame image, then perform denoising on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result, and perform denoising on the strong edge area based on the denoising area and the contrast area determined by the strong edge area to obtain a strong edge denoising result, and then determine the denoised image corresponding to the single-frame image based on the weak edge denoising result and the strong edge denoising result. By adopting the scheme of the present disclosure, by adopting different denoising methods for the strong edge area and the weak edge area to perform denoising, the effect of image denoising can be improved, and when denoising the strong edge area, denoising is performed based on the denoising area and the contrast area determined from the strong edge area, without processing multiple frames of images, which helps to improve the denoising efficiency, and also avoids the problem of ghosting caused by introducing multi-frame image synthesis, and ensures image quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a schematic diagram of a flow chart of an image noise reduction method in one embodiment;

[0052] Figure 2 is a schematic flow chart of an image noise reduction method in another embodiment;

[0053] Figure 3 is a schematic diagram of a Sobel convolution factor in one embodiment;

[0054] Figure 4 is a flow chart of an image noise reduction method in yet another embodiment;

[0055] Figure 5 is a structural block diagram of an image noise reduction device in one embodiment;

[0056] Figure 6 FIG. 4 is a diagram showing the internal structure of an electronic device in one embodiment. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.

[0058] Before explaining the solution of the present disclosure, the professional terms that may be involved in the present disclosure are explained as follows:

[0059] Image noise: refers to the unnecessary or irrelevant interference information in the image data. These noises are usually caused by random signal interference during the acquisition or transmission of the image, which hinders the understanding and analysis of the image.

[0060] Noise Reduction (NR): An image processing technique used to reduce noise or interference in an image, thereby improving the quality and clarity of the image;

[0061] Multiple Frame Noise Reduce (MFNR): Multiple photos are obtained in one shooting action. Pixels with different noise characteristics are found in different frames, and a relatively clean and clear photo is synthesized through weighted combination.

[0062] Sobel operator: Mainly used for edge detection, it is a first-order discrete difference operator used to calculate the grayscale approximation of the image brightness function. Using this operator at any point in the image will generate the corresponding grayscale vector or its normal vector.

[0063] Camera is a very popular application in current smart terminals, and many application scenarios can be derived based on Camera. With the improvement of people's living standards and the expansion of Camera application fields, people have higher and higher requirements for image effects. In image effects, people focus on the image presentation effect, and noise is a very important part that affects the imaging effect. When debugging the Camera, a large part of manpower and time is spent on noise reduction, so that the image finally displayed on mobile phones and other electronic devices is close to the effect seen by the human eye.

[0064] At present, the methods of noise reduction for Camera are mainly divided into two categories, namely single-frame noise reduction and multi-frame noise reduction.

[0065] Among them, single-frame noise reduction methods include median filtering, mean filtering, bilateral filtering, Gaussian filtering, etc. These noise reduction methods all perform overall noise reduction processing on the image of the same frame, or perform noise reduction in the frequency domain, such as wavelet transform, which converts the digital signal into the frequency domain and performs noise reduction on noises of different frequencies. However, it is difficult to control the degree of noise reduction in existing single-frame noise reduction methods. If the degree of noise reduction is too small, the noise reduction effect will be poor, while if the degree of noise reduction is too large, the picture details will be lost, resulting in poor picture quality.

[0066] Camera multi-frame noise reduction technology uses the characteristic of the camera taking multiple photos within a certain period of time, and fuses the multiple photos taken together through an algorithm, and finally obtains a photo with higher clarity and less noise. Its basic principle is to increase the signal-to-noise ratio of the photo by taking multiple photos of the same scene, thereby improving the quality of the photo, making the details clearer and the colors more realistic. However, camera multi-frame noise reduction requires taking multiple photos, so it is not suitable for scenes of shooting fast-moving objects, and it takes a long time to process multiple photos, which reduces the effect of rapid continuous shooting; if the photo is handheld, it is also easy to have inconsistent shooting postures, which will also lead to poor photo quality.

[0067] In view of the above problems, the present disclosure provides an image denoising method for denoising a single frame image, which can not only ensure a good denoising effect, but also has a better processing time than the traditional multi-frame denoising method.

[0068] The specific implementation process of the image noise reduction method, device, electronic device and storage medium provided by the present disclosure is described in detail below with reference to the accompanying drawings.

[0069] In one embodiment, Figure 1 As shown, a method for image noise reduction is provided. This embodiment mainly uses the image noise reduction method applied to an electronic device as an example, and the electronic device can be, but is not limited to, a mobile phone, a tablet computer, and other devices.

[0070] like Figure 1 As shown, the image noise reduction method may include the following steps:

[0071] Step 102: Obtain a single frame image to be processed.

[0072] The single-frame image to be processed may be an image currently taken by a camera of the electronic device, or the single-frame image to be processed may also be an image uploaded by a user, and the present disclosure does not impose any limitation on this.

[0073] Step 104: perform edge detection on the single-frame image to determine the weak edge region and the strong edge region of the single-frame image.

[0074] In the embodiment of the present disclosure, edge detection can be performed on the acquired single frame image to determine the weak edge region and the strong edge region of the single frame image, wherein the gradient value of the pixel point in the weak edge region is smaller than the gradient value of the pixel point in the strong edge region.

[0075] As an example, a currently commonly used edge detection algorithm can be used to perform edge detection on the acquired single-frame image to obtain the edge portion in the single-frame image, and then the area where the edge portion is located is determined as a strong edge area, and the other areas in the single-frame image except the edge portion are determined as weak edge areas.

[0076] Step 106: Perform noise reduction processing on the weak edge area based on a preset weak edge noise reduction algorithm to obtain a weak edge noise reduction result.

[0077] In the disclosed embodiment, for the determined weak edge area in the single frame image, a preset weak edge denoising algorithm may be used to perform denoising processing on it to obtain a weak edge denoising result.

[0078] Among them, the preset weak edge denoising algorithm can be a commonly used single-frame denoising algorithm, including but not limited to median filtering, mean filtering, bilateral filtering, Gaussian filtering, wavelet transform, etc.

[0079] Step 108 , performing noise reduction processing on the strong edge area based on the noise reduction area and the comparison area determined in the strong edge area to obtain a strong edge noise reduction result.

[0080] The sizes of the noise reduction area and the contrast area are the same.

[0081] In the disclosed embodiment, when performing noise reduction on a strong edge region, a noise reduction area and a comparison area may be determined based on the strong edge region, and the noise reduction area may be subjected to noise reduction processing using the comparison area to obtain a strong edge noise reduction result.

[0082] As an example, a noise reduction area and multiple comparison areas can be determined from the strong edge area according to a preset size, the similarity between each comparison area and the noise reduction area is calculated, and all comparison areas with similarities greater than a threshold are determined for noise reduction processing on the noise reduction area to obtain the noise reduction result corresponding to the noise reduction area. The noise reduction area determined multiple times traverses the entire strong edge area, that is, multiple noise reduction areas are combined into a strong edge area, and the noise reduction results corresponding to each noise reduction area are combined to obtain the strong edge noise reduction result.

[0083] It can be understood that using the contrast area to perform noise reduction on the noise reduction area is to average or maximize the pixel values ​​of the pixels at the same position in the noise reduction area and the contrast area, and obtain a new pixel value of the pixel at the corresponding position, which is the result of noise reduction at that position. The obtained new pixel value can be used to replace the pixel of the corresponding pixel in the noise reduction area, and finally obtain the noise reduction result of the noise reduction area; or, the new pixel value can be filled into the corresponding pixel position in a blank image, and the blank image is consistent with the shape and size of the strong edge area (or noise reduction area), and finally obtain the strong edge area (or noise reduction area) after noise reduction.

[0084] It should be noted that, in this embodiment, step 106 and step 108 may be performed successively or simultaneously. Figure 1 The illustrated embodiment only uses step 108 executed after step 106 as an example to explain the present disclosure, and cannot be used as a limitation of the present disclosure. It can be understood that both the weak edge denoising result and the strong edge denoising result include multiple pixels, and the position information of each pixel still retains its pixel position in the single-frame image. For example, the first pixel in the weak edge area is the pixel in the third row and second column in the single-frame image, and the position information is (3,2), then the position of the first pixel in the weak edge area is still retained as (3,2).

[0085] Step 110: Determine a denoised image corresponding to the single frame image based on the weak edge denoising result and the strong edge denoising result.

[0086] In the disclosed embodiment, after denoising the weak edge area to obtain a weak edge denoising result, and denoising the strong edge area to obtain a strong edge denoising result, the denoised image corresponding to the single frame image can be determined based on the weak edge denoising result and the strong edge denoising result, thereby completing the denoising process on the single frame image.

[0087] As an example, the corresponding denoising result (i.e., the pixel value after denoising) can be updated to the pixel corresponding to the corresponding position information in the single-frame image according to the position information of each pixel in the weak edge denoising result and the strong edge denoising result to obtain a denoised image.

[0088] As another example, the weak edge denoising result and the strong edge denoising result may be spliced ​​according to the position information of each pixel in the weak edge denoising result and the strong edge denoising result to obtain a denoised image.

[0089] The image denoising method provided by the embodiment of the present disclosure obtains a single-frame image to be processed, performs edge detection on the single-frame image, determines the weak edge area and the strong edge area of ​​the single-frame image, then performs denoising on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result, and performs denoising on the strong edge area based on the denoising area and the contrast area determined by the strong edge area to obtain a strong edge denoising result, and then determines the denoised image corresponding to the single-frame image based on the weak edge denoising result and the strong edge denoising result. The scheme of the present disclosure can improve the effect of image denoising by adopting different denoising methods for the strong edge area and the weak edge area, and when denoising the strong edge area, denoising is performed based on the denoising area and the contrast area determined from the strong edge area, without processing multiple frames of images, which helps to improve the denoising efficiency, and also avoids the problem of ghosting caused by introducing multi-frame image synthesis, thereby ensuring image quality.

[0090] In an optional embodiment of the present disclosure, Figure 2 As shown, in Figure 1 Based on the illustrated embodiment, step 104 may include the following sub-steps:

[0091] Step 202, grayscale processing is performed on the single frame image to obtain a grayscale image.

[0092] Exemplarily, a common grayscale processing method can be used to grayscale a single frame image to obtain a grayscale image. For example, a maximum value method, an average value method, a weighted average method, a component method, etc. can be used to convert a single frame image into a grayscale image. By converting a single frame image into a grayscale image and subsequently processing based on the grayscale image, the amount of calculation can be reduced, which is convenient for subsequent edge detection.

[0093] Step 204 , edge detection is performed on the grayscale image based on a preset edge detection algorithm to determine an edge detection result, where the edge detection result includes the coordinates of the edge points and the gradient values ​​corresponding to the edge points.

[0094] The preset edge detection algorithm may be any one of the currently commonly used edge detection algorithms, including but not limited to Roberts Cross operator, Prewitt operator, Sobel operator, Canny operator, compass operator, Krisch operator, and Laplacian operator.

[0095] In this embodiment, after obtaining the grayscale image, an edge detection algorithm may be used to perform edge detection on the grayscale image to obtain an edge detection result, where the edge detection result includes the coordinates of edge points and the gradient values ​​corresponding to the edge points.

[0096] Taking the preset edge detection algorithm as Sobel operator as an example, the Sobel operator is used to detect the edge of the grayscale image. The Sobel operator is a first-order reciprocal edge detection operator. It uses a 3×3 matrix as the kernel to perform convolution and operation with each pixel in the grayscale image, and then sets a suitable threshold to select the edge.

[0097] The Sobel operator contains two sets of 3×3 matrices as convolution factors, namely the horizontal matrix and the vertical matrix. The Sobel convolution factor Figure 3 As shown. Figure 3 The convolution factors in the two directions shown in the figure are used to perform planar convolution on the grayscale image, and the approximate brightness difference in the horizontal and vertical directions can be obtained respectively. Assuming that the grayscale image is A, Gx and Gy represent the approximate brightness difference in the horizontal and vertical directions respectively, then:

[0098]

[0099] Assuming f(x,y) represents the grayscale value of the pixel (x,y) in the grayscale image, the Gx and Gy corresponding to the pixel (x,y) are:

[0100] Gx=(-1)*f(x-1,y-1)+0*f(x,y-1)+1*f(x+1,y-1)+(-2)*f(x-1,y)+0*f(x,y)+2*f(x+1,y)+(-1)*f(x-1,y+1)+ 0*f(x,y+1)+1*f(x+1,y+1)=[f(x+1,y-1)+2*f(x+1,y)+f(x+1,y+1)]-[f(x-1,y-1)+2*f(x-1,y)+f(x-1,y+1)];

[0101] Gy=1*f(x-1,y-1)+2*f(x,y-1)+1*f(x+1,y-1)+0*f(x-1,y)0*f(x,y)+0*f(x+1,y)+(-1)*f(x-1,y+1)+(-2)*f (x,y+1)+(-1)*f(x+1,y+1)=[f(x-1,y-1)+2f(x,y-1)+f(x+1,y-1)]-[f(x-1,y+1)+2*f(x,y+1)+f(x+1,y+1)].

[0102] For the Gx and Gy corresponding to the obtained pixel point (x, y), the gradient value corresponding to the pixel point can be determined by the following formula:

[0103] |G|=|Gx|+|Gy|.

[0104] Then, the obtained G is compared with a preset threshold. If it is greater than the threshold, it means that the point is an edge point. The threshold can be used as an external parameter of the algorithm and dynamically adjusted according to different scenarios.

[0105] Through the above process, all edge points in the grayscale image can be identified and the edge detection result can be obtained.

[0106] Step 206 , determining weak edge points and strong edge points based on the gradient value of each edge point in the edge detection result and a preset gradient threshold.

[0107] Among them, the preset gradient threshold can be set according to actual needs and can be dynamically adjusted according to different scenarios.

[0108] In this embodiment, after obtaining the edge detection result, the gradient value of each edge point in the edge detection result can be compared with the preset gradient threshold to determine the weak edge point and the strong edge point. If the gradient value of an edge point is greater than the preset gradient threshold, the edge point is determined to be a strong edge point, and if the gradient value of an edge point is not greater than the preset gradient threshold, the edge point is determined to be a weak edge point.

[0109] Step 208: determining a weak edge region of the single frame image based on the coordinates of the weak edge points, and determining a strong edge region of the single frame image based on the coordinates of the strong edge points.

[0110] In the disclosed embodiment, after the weak edge points and the strong edge points are determined, the weak edge region of the single frame image can be determined based on the coordinates of the weak edge points, and the strong edge region of the single frame image can be determined based on the coordinates of the strong edge points.

[0111] As an example, the largest area surrounded by multiple weak edge points and not including strong edge points can be determined as a weak edge area. Similarly, the largest area surrounded by multiple strong edge points and not including weak edge points can be determined as a strong edge area. In addition, it should be noted that in this embodiment, other areas (i.e., flat areas) in a single-frame image except for the weak edge area and the strong edge area are also determined to be weak edge areas.

[0112] In addition, it should be noted that, in this embodiment, for the special case where the single-frame image is a pure color scene, that is, there is no strong edge in the single-frame image, the entire area of ​​the single-frame image is considered to be a weak edge area, and noise reduction can be performed using traditional single-frame noise reduction algorithms, such as bilateral filtering.

[0113] The image denoising method of the disclosed embodiment performs grayscale processing on a single-frame image to obtain a grayscale image, performs edge detection on the grayscale image based on a preset edge detection algorithm, and determines an edge detection result, wherein the edge detection result includes the coordinates of the edge point and the gradient value corresponding to the edge point. Then, based on the gradient value of each edge point in the edge detection result and a preset gradient threshold, weak edge points and strong edge points are determined, and then based on the coordinates of the weak edge points, the weak edge area of ​​the single-frame image is determined, and based on the coordinates of the strong edge points, the strong edge area of ​​the single-frame image is determined. In this way, the division of the strong edge area and the weak edge area in the single-frame image is achieved, which provides conditions for performing denoising using different denoising methods.

[0114] In an optional embodiment of the present disclosure, Figure 4 As shown, in Figure 1 Based on the illustrated embodiment, step 108 may include the following sub-steps:

[0115] Step 302: determine a plurality of pixel points from the strong edge region.

[0116] As an example, all pixel points in the strong edge region may be acquired to obtain a plurality of pixel points.

[0117] As another example, starting from the first pixel point in the strong edge area (with the smallest horizontal coordinate and / or the smallest vertical coordinate), the pixel points in the strong edge area can be sampled according to a preset step size (e.g., 3 pixel points) to obtain multiple pixel points. For example, assuming that the preset step size is 2 pixel points, the first point, the third point, the fifth point, etc. are sampled, and so on. Among them, when sampling, sampling can be performed in order from left to right and from top to bottom. Among them, the preset step size can be set according to actual needs. Usually, the preset step size is not greater than the smaller value of the length and width of the determined noise reduction area to ensure that every point in the strong edge area can be denoised.

[0118] Step 304 , traverse each pixel point among the multiple pixel points, take the currently traversed pixel point as the center point, and determine the noise reduction area from the strong edge area according to the preset length and preset width.

[0119] The preset length and preset width can be set according to actual needs. For example, the preset length can be set to 1% of the length of a single frame image, and the preset width can be set to 1% of the width of a single frame image.

[0120] In the disclosed embodiment, for the multiple pixel points in the acquired strong edge area, each pixel point can be traversed in turn, and the currently traversed pixel point can be used as the center point to determine the noise reduction area from the strong edge area according to the preset length and preset width.

[0121] It should be noted that, in this embodiment, if the strong edge area is located at the edge of a single-frame image, the currently traversed pixel point may also be located at the edge of the single-frame image. When determining the noise reduction area, if the currently traversed pixel point is located at the edge of the single-frame image, it can be extended to the non-edge direction of the image, that is, the non-edge points adjacent to the currently traversed pixel point are copied to the other side, and then the noise reduction area is determined with the currently traversed pixel point as the center point. For example, if the currently traversed pixel point is located at the right edge of the single-frame image, the pixels on the left side of the currently traversed pixel point are copied to its right side, and then the noise reduction area is determined with the currently traversed pixel point as the center point.

[0122] Step 306 , performing noise reduction processing on the noise reduction area based on multiple comparison areas, wherein each comparison area is determined from the strong edge area according to a preset length and a preset width, and the center point of the comparison area is not included in the noise reduction area.

[0123] In the disclosed embodiment, each time a noise reduction area is determined, multiple comparison areas need to be obtained for performing noise reduction processing on the noise reduction area, wherein the comparison area is also determined from the strong edge area according to a preset length and a preset width, the size of the comparison area is consistent with the noise reduction area, and the center point of the comparison area is not included in the noise reduction area.

[0124] As an optional implementation, performing noise reduction processing on the noise reduction area based on multiple contrast areas may include: determining a first preset number of contrast areas from the strong edge area, wherein the first preset number is greater than 1, calculating the similarity between each contrast area and the noise reduction area, and then, based on the similarity, determining a target contrast area having a similarity greater than a preset value from the first preset number of contrast areas, and then performing noise reduction processing on the noise reduction area based on the target contrast area.

[0125] Among them, the currently commonly used image similarity calculation method can be used to calculate the similarity between the noise reduction area and each comparison area, and the present disclosure does not limit the similarity calculation method.

[0126] For example, the Structural Similarity Index measure (SSIM) algorithm can be used to calculate the similarity between the noise reduction area and the comparison area. The result calculated by the SSIM algorithm ranges between [0, 1]. The closer the value is to 1, the higher the similarity between the two images. The specific implementation of the SSIM algorithm is as follows:

[0127]

[0128] Wherein, x and y represent two images to be compared, and in this embodiment, represent the noise reduction area and the comparison area; μ x With μ yRespectively represent the mean values ​​of the x image and the y image, and in this embodiment represent the mean values ​​corresponding to the noise reduction area and the contrast area respectively; σ x 2 With σ y 2 They represent the variances of the x image and the y image respectively, and in this embodiment, they represent the variances corresponding to the noise reduction area and the contrast area respectively; σ xy represents the covariance of the two images, and in this embodiment represents the covariance of the noise reduction area and the contrast area; C1 and C2 are constants, and their main function is to prevent the denominator from being zero. The value ranges of these two constants vary depending on the color range. The calculation formula of C1 is C1=(K1*L) 2 , the calculation formula of C2 is C2=(K2*L) 2 , where L is the value range of the pixel, L-1 is the maximum pixel value, and the calculation formula of L is L=2 B -1, B is the bit depth, currently 8 bits are commonly used, so the L value is generally 255. By default, K1=0.01, K2=0.03.

[0129] After determining the similarity between the noise reduction area and each comparison area, each similarity can be compared with a preset value to determine a target comparison area with a similarity greater than the preset value, and then the noise reduction area is subjected to noise reduction processing based on the target comparison area. The specific value of the preset value can be set according to actual needs, for example, the preset value can be set to 0.8.

[0130] In an optional embodiment of the present disclosure, performing noise reduction processing on the noise reduction area based on multiple comparison areas may include: determining the current comparison area from the strong edge area, calculating the similarity between the current comparison area and the noise reduction area, and then counting the number of target comparison areas in the determined comparison areas whose similarity is greater than a similarity threshold, and when the number of target comparison areas reaches a second preset number, performing noise reduction processing on the noise reduction area based on the target comparison areas.

[0131] Among them, the similarity threshold can be set according to actual needs and can be dynamically adjusted according to different scenarios. For example, the similarity threshold can be set to 0.8; the second preset number can also be set according to actual needs. For example, the second preset number can be set to 5. That is to say, when the similarity between 5 comparison areas in the determined comparison areas and the noise reduction area is greater than the similarity threshold, the operation of determining the comparison area from the strong edge area is stopped, and the noise reduction area can be subjected to noise reduction processing using these 5 comparison areas, thereby improving the noise reduction efficiency. The similarity calculation can adopt a commonly used image similarity calculation method such as the SSIM algorithm, and the present disclosure does not limit this.

[0132] That is, in this embodiment, for the currently determined noise reduction area, one comparison area is determined at a time to calculate the similarity, and after each calculation, the number of comparison areas determined for the noise reduction area with a similarity greater than the similarity threshold is counted. If the number of comparison areas with a similarity greater than the similarity threshold does not reach the second preset number, a comparison area is determined from the strong edge area again and the similarity with the noise reduction area is calculated, and the number of comparison areas determined for the noise reduction area with a similarity greater than the similarity threshold is counted again. If it still does not reach the second preset number, a comparison area is determined from the strong edge area again and the similarity with the noise reduction area is calculated, and the above process is repeated until the number of comparison areas determined for the noise reduction area with a similarity greater than the similarity threshold reaches the second preset number, and the determination of new comparison areas is stopped, and the comparison areas with a similarity greater than the similarity threshold in the determined comparison areas are determined as target comparison areas for noise reduction processing of the noise reduction area. In this way, the number of determined comparison areas can be reduced, and the number of target comparison areas can be controlled, thereby improving noise reduction efficiency and reducing the amount of calculation.

[0133] In this embodiment, when the noise reduction area is subjected to noise reduction processing based on the target contrast area, the noise reduction area can be used as a base frame, and the target contrast area can be used to perform mean calculation on the noise reduction area (or weighted average, the weight corresponding to the noise reduction area can be set larger than the weight corresponding to the target contrast area, and the sum of all weights is 1), and the calculation result is used as the result after noise reduction to achieve noise reduction of the noise reduction area. For example, for the pixel point in the first row and first column of the noise reduction area, the pixel value of the point in the noise reduction area is averaged with the pixel value of the pixel point in the first row and first column of each target contrast area, and the obtained average is used as the noise reduction result of the pixel point in the first row and first column of the noise reduction area. Each pixel point in the noise reduction area is processed in the above manner, and the noise reduction result corresponding to the noise reduction area is obtained.

[0134] Step 308: After traversing multiple pixel points, a strong edge denoising result is obtained.

[0135] It can be understood that in the present embodiment, steps 304 to 306 are executed in a loop. When the noise reduction area is determined based on the currently traversed pixel point and the noise reduction process is performed using the comparison area, step 304 is continued to be executed to determine the next pixel point among the multiple pixels as the currently traversed pixel point, and the noise reduction area is determined based on the currently traversed pixel point and the noise reduction process is performed using the comparison area. After the noise reduction is completed, step 304 is executed again to determine a new currently traversed pixel point, and this process is repeated until all the pixel points in the multiple pixel points are traversed, so that all the points in the strong edge area are denoised, and the strong edge noise reduction result corresponding to the strong edge area is obtained.

[0136] In an optional implementation of the present disclosure, a strong edge area will determine multiple noise reduction areas, and there may be overlapping pixels between the noise reduction areas. These pixels will be processed by noise reduction in each noise reduction area to obtain a noise reduction result, that is, a pixel in a strong edge area may obtain multiple noise reduction results. For this case, the final noise reduction result can be obtained by calculating the average of multiple values. Alternatively, after obtaining the noise reduction result of a pixel for the first time, the noise reduction result can be used as the final noise reduction result of the pixel, and the noise reduction result of the same pixel calculated again later is directly discarded. Alternatively, for a pixel, the noise reduction result obtained last time can be updated with the new noise reduction result obtained each time, until the noise reduction result of the pixel is no longer obtained, and the final noise reduction result is obtained, that is, the noise reduction result obtained by the last calculation is used as the final noise reduction result of the pixel. Alternatively, other processing methods can be used to process overlapping pixels, and the present disclosure does not limit this.

[0137] It should be noted that, in this embodiment, there may be at least one strong edge region. When there are multiple strong edge regions, steps 302 to 308 are performed for each strong edge region to implement noise reduction processing for each strong edge region.

[0138] The image denoising method of the disclosed embodiment determines a plurality of pixels from a strong edge region, traverses each of the plurality of pixels, takes the currently traversed pixel as a center point, determines a denoising region from the strong edge region according to a preset length and a preset width, and then performs denoising on the denoising region based on a plurality of contrast regions, wherein each contrast region is determined from the strong edge region according to a preset length and a preset width, and the currently traversed pixel is not included in the contrast region, and after traversing the plurality of pixels, a strong edge denoising result is obtained, thereby, by determining a contrast region from the strong edge region for denoising the denoising region, the texture and details of the image can be better preserved, so that the denoising method of the present scheme is not only more efficient than the multi-frame denoising method, but also has a better denoising effect than the traditional single-frame denoising, and the present scheme makes full use of the redundant information of the image itself for single-frame denoising, and does not introduce ghosting or new image quality problems of multi-frame synthesis, thereby ensuring the denoising effect while also ensuring the image quality.

[0139] It should be understood that although Figure 1-4 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-4At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0140] In one embodiment, Figure 5 As shown, an image denoising device is provided, the image denoising device 50 comprises an image acquisition module 502, an edge detection module 504, a weak edge denoising module 506, a strong edge denoising module 508 and a result fusion module 510. Among them:

[0141] An image acquisition module 502 is used to acquire a single frame image to be processed;

[0142] An edge detection module 504 is used to perform edge detection on the single frame image to determine a weak edge region and a strong edge region of the single frame image;

[0143] A weak edge denoising module 506 is used to perform denoising processing on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result;

[0144] A strong edge denoising module 508 is used to perform denoising processing on the strong edge region based on the denoising region and the contrast region determined in the strong edge region to obtain a strong edge denoising result;

[0145] The result fusion module 510 is used to determine the denoised image corresponding to the single frame image based on the weak edge denoising result and the strong edge denoising result.

[0146] In one embodiment, the edge detection module 504 is further configured to:

[0147] Performing grayscale processing on the single-frame image to obtain a grayscale image;

[0148] Performing edge detection on the grayscale image based on a preset edge detection algorithm to determine an edge detection result, wherein the edge detection result includes the coordinates of the edge points and the gradient values ​​corresponding to the edge points;

[0149] Determine weak edge points and strong edge points based on the gradient value of each edge point in the edge detection result and a preset gradient threshold;

[0150] A weak edge region of the single frame image is determined based on the coordinates of the weak edge points, and a strong edge region of the single frame image is determined based on the coordinates of the strong edge points.

[0151] In one embodiment, the strong edge noise reduction module 508 includes:

[0152] A first determining unit, configured to determine a plurality of pixel points from the strong edge region;

[0153] A second determining unit is used to traverse each pixel point of the plurality of pixel points, take the currently traversed pixel point as the center point, and determine the noise reduction area from the strong edge area according to a preset length and a preset width;

[0154] a noise reduction unit, configured to perform noise reduction processing on the noise reduction area based on a plurality of comparison areas, wherein each comparison area is determined from the strong edge area according to the preset length and the preset width, and the comparison area does not contain the currently traversed pixel point;

[0155] The result determination unit is used to obtain the strong edge denoising result after traversing the multiple pixel points.

[0156] Furthermore, in one embodiment, the noise reduction unit is further configured to:

[0157] Determine a first preset number of contrast regions from the strong edge region, wherein the first preset number is greater than 1;

[0158] Calculating the similarity between each of the comparison areas and the noise reduction area;

[0159] According to the similarity, determining a target comparison area having a similarity greater than a preset value from the first preset number of comparison areas;

[0160] The noise reduction area is subjected to noise reduction processing based on the target comparison area.

[0161] In one embodiment, the noise reduction unit is further configured to:

[0162] Determine a current contrast region from the strong edge region;

[0163] Calculating the similarity between the current comparison area and the noise reduction area;

[0164] Counting the number of target comparison areas whose similarities in the determined comparison areas are greater than a similarity threshold;

[0165] When the number of the target comparison areas reaches a second preset number, noise reduction processing is performed on the noise reduction area based on the target comparison areas.

[0166] The image denoising device provided by the embodiment of the present disclosure obtains a single-frame image to be processed, performs edge detection on the single-frame image, determines the weak edge area and the strong edge area of ​​the single-frame image, then performs denoising on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result, and performs denoising on the strong edge area based on the denoising area and the contrast area determined by the strong edge area to obtain a strong edge denoising result, and then determines the denoised image corresponding to the single-frame image based on the weak edge denoising result and the strong edge denoising result. The scheme of the present disclosure can improve the effect of image denoising by adopting different denoising methods for the strong edge area and the weak edge area, and when denoising the strong edge area, denoising is performed based on the denoising area and the contrast area determined from the strong edge area, without processing multiple frames of images, which helps to improve the denoising efficiency, avoids the problem of ghosting caused by introducing multi-frame image synthesis, and ensures image quality.

[0167] For the specific definition of the image denoising device, please refer to the definition of the image denoising method above, which will not be repeated here. Each module in the above-mentioned image denoising device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0168] In one embodiment, an electronic device is provided. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown. The electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, an image noise reduction method is implemented. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0169] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0170] In one embodiment, the image noise reduction device provided by the present disclosure can be implemented in the form of a computer program. The computer program can be Figure 6 The memory of the electronic device may store various program modules constituting the image noise reduction device, for example, Figure 5 The image acquisition module, edge detection module, weak edge denoising module, strong edge denoising module and result fusion module shown in the figure are computer programs composed of various program modules, which enable the processor to execute the steps of the image denoising method of various embodiments of the present disclosure described in this specification.

[0171] For example, Figure 6 The electronic device shown can be Figure 5 The image acquisition module in the image denoising device shown executes the step of acquiring the single frame image to be processed. The electronic device may perform edge detection on the single frame image through the edge detection module to determine the weak edge area and the strong edge area of ​​the single frame image. The electronic device may perform denoising processing on the weak edge area based on a preset weak edge denoising algorithm through the weak edge denoising module to obtain the weak edge denoising result. The electronic device may perform denoising processing on the strong edge area based on the denoising area and the contrast area determined by the strong edge area through the strong edge denoising module to obtain the strong edge denoising result. The electronic device may perform the step of determining the denoised image corresponding to the single frame image based on the weak edge denoising result and the strong edge denoising result through the result fusion module.

[0172] In one embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0173] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0174] In one embodiment, a computer program product is further provided, wherein the computer program product is used to execute the steps in the above method embodiments.

[0175] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static random access memory (SRAM) and dynamic random access memory (DRAM).

[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The above-mentioned embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the patent of the present disclosure shall be subject to the attached claims.

Claims

1. An image denoising method, characterized in that: The method comprises: Obtain a single frame image to be processed; Performing edge detection on the single-frame image to determine a weak edge region and a strong edge region of the single-frame image; Performing denoising processing on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result; Performing noise reduction processing on the strong edge area based on the noise reduction area and the comparison area determined in the strong edge area to obtain a strong edge noise reduction result; Based on the weak edge denoising result and the strong edge denoising result, a denoised image corresponding to the single frame image is determined.

2. The method according to claim 1, characterized in that The performing edge detection on the single frame image to determine the weak edge area and the strong edge area of ​​the single frame image includes: Performing grayscale processing on the single-frame image to obtain a grayscale image; Perform edge detection on the grayscale image based on a preset edge detection algorithm to determine an edge detection result, wherein the edge detection result includes the coordinates of the edge point and the gradient value corresponding to the edge point; Determine weak edge points and strong edge points based on the gradient value of each edge point in the edge detection result and a preset gradient threshold; A weak edge region of the single frame image is determined based on the coordinates of the weak edge points, and a strong edge region of the single frame image is determined based on the coordinates of the strong edge points.

3. The method according to claim 1, characterized in that The denoising area and the contrast area determined based on the strong edge area are subjected to denoising processing on the strong edge area to obtain a strong edge denoising result, including: Determine a plurality of pixel points from the strong edge region; Traversing each pixel point among the plurality of pixel points, taking the currently traversed pixel point as the center point, and determining the noise reduction area from the strong edge area according to a preset length and a preset width; Performing noise reduction processing on the noise reduction area based on a plurality of contrasting areas, wherein each contrasting area is determined from the strong edge area according to the preset length and the preset width, and a center point of the contrasting area is not included in the noise reduction area; After traversing the plurality of pixel points, the strong edge denoising result is obtained.

4. The method according to claim 3, characterized in that The performing noise reduction processing on the noise reduction area based on the multiple comparison areas includes: Determine a first preset number of contrast regions from the strong edge region, wherein the first preset number is greater than 1; Calculating the similarity between each of the comparison areas and the noise reduction area; According to the similarity, determining a target comparison area having a similarity greater than a preset value from the first preset number of comparison areas; The noise reduction area is subjected to noise reduction processing based on the target comparison area.

5. The method according to claim 3, characterized in that: The performing noise reduction processing on the noise reduction area based on the multiple comparison areas includes: Determine a current contrast region from the strong edge region; Calculating the similarity between the current comparison area and the noise reduction area; Counting the number of target comparison areas whose similarity in the determined comparison areas is greater than a similarity threshold; When the number of the target comparison areas reaches a second preset number, noise reduction processing is performed on the noise reduction area based on the target comparison areas.

6. An image noise reduction device, characterized in that: include: An image acquisition module, used to acquire a single frame image to be processed; An edge detection module, used to perform edge detection on the single frame image to determine a weak edge region and a strong edge region of the single frame image; A weak edge denoising module, used to perform denoising processing on the weak edge area based on a preset weak edge denoising algorithm to obtain a weak edge denoising result; A strong edge denoising module, used for performing denoising processing on the strong edge area based on the denoising area and the contrast area determined in the strong edge area, to obtain a strong edge denoising result; A result fusion module is used to determine a denoised image corresponding to the single frame image based on the weak edge denoising result and the strong edge denoising result.

7. The device according to claim 6, characterized in that The edge detection module is further used for: Performing grayscale processing on the single-frame image to obtain a grayscale image; Performing edge detection on the grayscale image based on a preset edge detection algorithm to determine an edge detection result, wherein the edge detection result includes the coordinates of the edge points and the gradient values ​​corresponding to the edge points; Determine weak edge points and strong edge points based on the gradient value of each edge point in the edge detection result and a preset gradient threshold; A weak edge region of the single frame image is determined based on the coordinates of the weak edge points, and a strong edge region of the single frame image is determined based on the coordinates of the strong edge points.

8. The device according to claim 6, characterized in that The strong edge noise reduction module comprises: A first determining unit, configured to determine a plurality of pixel points from the strong edge region; A second determining unit is used to traverse each pixel point of the plurality of pixel points, take the currently traversed pixel point as the center point, and determine the noise reduction area from the strong edge area according to a preset length and a preset width; a noise reduction unit, configured to perform noise reduction processing on the noise reduction area based on a plurality of comparison areas, wherein each comparison area is determined from the strong edge area according to the preset length and the preset width, and the comparison area does not contain the currently traversed pixel point; The result determination unit is used to obtain the strong edge denoising result after traversing the multiple pixel points.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the image noise reduction method according to any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the image noise reduction method according to any one of claims 1 to 5 are implemented.

Images