Image quality enhancement method, device and equipment based on speckle noise suppression

Abstract

The present invention provides an image quality enhancement method, device and equipment based on speckle noise suppression, which relates to the field of image processing technology, including: obtaining a SAR image to be enhanced; extracting a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced based on the position of each pixel to be enhanced in the SAR image to be enhanced; determining similar pixels corresponding to each pixel to be enhanced in the SAR image to be enhanced according to the gray value matrix; using the similar pixels corresponding to each pixel to be enhanced, performing speckle noise suppression filtering on the SAR image to be enhanced to obtain a target SAR image. The present invention can better retain the texture details in the image, thereby significantly improving the clarity of the image after quality enhancement.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an image quality enhancement method, device and equipment based on speckle noise suppression. Background Art

[0002] Due to the limited resolution and coherence of the SAR (Synthetic Aperture Radar) system, a kind of coherent speckle noise is always inevitable in the SAR imaging process. Essentially different from the noise encountered in digital image processing, the speckle noise in SAR images is generated in the radar echo signal, which is an inherent principle defect of all coherent imaging systems including SAR systems. The traditional non-local mean filtering algorithm uses a fixed square window or circular window image block of the same size, and simply presents the similarity through the Euclidean distance function determined by the original gray value of the pixel, so that the area with large similarity between neighbors obtains a small weight, which leads to defects such as loss of image details and image blur. Summary of the invention

[0003] In view of this, the object of the present invention is to provide an image quality enhancement method, device and equipment based on speckle noise suppression, which can better preserve the texture details in the image, thereby significantly improving the clarity of the image after quality enhancement.

[0004] In a first aspect, an embodiment of the present invention provides an image quality enhancement method based on speckle noise suppression, comprising:

[0005] Acquire the SAR image to be enhanced;

[0006] Based on the position of each pixel to be enhanced in the SAR image to be enhanced, extracting a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced;

[0007] Determine, according to the gray value matrix, similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced;

[0008] The similar pixel points corresponding to each pixel point to be enhanced are used to perform speckle noise suppression filtering on the SAR image to be enhanced to obtain a target SAR image.

[0009] In one implementation, based on the position of each pixel to be enhanced in the SAR image to be enhanced, the step of extracting a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced includes:

[0010] For each pixel to be enhanced in the SAR image to be enhanced, taking the position of the pixel to be enhanced as the center, extracting an adjacent area corresponding to the pixel to be enhanced from the SAR image to be enhanced based on a preset window size;

[0011] Based on the grayscale values ​​corresponding to the pixels in the adjacent area, a grayscale value matrix corresponding to the pixel to be enhanced is constructed.

[0012] In one implementation, the step of determining similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced according to the gray value matrix includes:

[0013] For each pixel to be enhanced in the SAR image to be enhanced, determining, according to the gray value matrix, a distance between the pixel to be enhanced and other pixels in the SAR image to be enhanced;

[0014] If the distance between the pixel to be enhanced and the other pixel points is less than a preset threshold, the other pixel points are determined as similar pixel points corresponding to the pixel to be enhanced.

[0015] In one implementation, the step of determining the distance between the pixel to be enhanced and other pixel points in the SAR image to be enhanced according to the gray value matrix includes:

[0016] Using the gray value matrix corresponding to the pixel to be enhanced as a first gray value matrix, and using the gray value matrix corresponding to other pixel points in the SAR image to be enhanced as a second gray value matrix;

[0017] Determine a covariance matrix between the first gray value matrix and the second gray value matrix; wherein the covariance matrix is ​​used to describe the correlation between the first gray value matrix and the second gray value matrix;

[0018] The distance between the pixel to be enhanced and the other pixel points is determined according to the first gray value matrix, the second gray value matrix and the inverse matrix of the covariance matrix.

[0019] In one implementation, the step of determining the distance between the pixel to be enhanced and the other pixel points according to the first gray value matrix, the second gray value matrix and the inverse matrix of the covariance matrix includes:

[0020] Determine a matrix difference between the first gray value matrix and the second gray value matrix;

[0021] Calculating a matrix product between the transpose of the matrix difference, the inverse of the covariance matrix, and the matrix difference;

[0022] The square root value of the matrix product is used as the distance between the pixel to be enhanced and the other pixel points.

[0023] In one implementation, the step of performing speckle noise suppression filtering on the SAR image to be enhanced by using the similar pixel points corresponding to each pixel point to be enhanced to obtain a target SAR image includes:

[0024] Determine a weight value between the pixel to be enhanced and the similar pixel according to the distance between the pixel to be enhanced and the similar pixel; wherein the weight value is negatively correlated with the distance;

[0025] Based on the weight value, weighted average is performed on the grayscale values ​​corresponding to the similar pixels to obtain the target grayscale value corresponding to the pixel to be enhanced;

[0026] The target grayscale value corresponding to the pixel point to be enhanced is assigned to the SAR image to be enhanced, so as to suppress speckle noise of the SAR image to be enhanced and obtain a target SAR image.

[0027] In one implementation, the step of determining the weight value between the pixel to be enhanced and the similar pixel according to the distance between the pixel to be enhanced and the similar pixel includes:

[0028] Determine a smoothing parameter in a weight function according to the gray value standard deviation and the number of pixels corresponding to the similar pixels; wherein the smoothing parameter is used to control the decay speed of the weight function, and the weight function is the ratio of a kernel function to a normalization function;

[0029] The smoothing parameter and the distance between the pixel to be enhanced and the similar pixel are substituted into the weight function to obtain a weight value between the pixel to be enhanced and the similar pixel.

[0030] In a second aspect, an embodiment of the present invention further provides an image quality enhancement device based on speckle noise suppression, comprising:

[0031] An image acquisition module, used for acquiring the SAR image to be enhanced;

[0032] A grayscale extraction module, configured to extract a grayscale value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced based on the position of each pixel to be enhanced in the SAR image to be enhanced;

[0033] A similar point determination module, used for determining similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced according to the gray value matrix;

[0034] The noise suppression module is used to perform speckle noise suppression filtering on the SAR image to be enhanced by using the similar pixel points corresponding to each pixel point to be enhanced to obtain a target SAR image.

[0035] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0036] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0037] The embodiment of the present invention provides an image quality enhancement method, device and equipment based on speckle noise suppression. First, a SAR image to be enhanced is obtained; then, based on the position of each pixel to be enhanced in the SAR image to be enhanced, a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced is extracted; then, based on the gray value matrix, similar pixels corresponding to each pixel to be enhanced in the SAR image to be enhanced are determined; finally, the similar pixels corresponding to each pixel to be enhanced are used to perform speckle noise suppression filtering on the SAR image to be enhanced, so as to obtain a target SAR image. The above method extracts the corresponding gray value matrix according to the position of the pixel to be enhanced, fully considers the pattern redundancy of the SAR image to be enhanced, and on this basis, the similar pixels corresponding to the pixel to be enhanced are determined using the gray value matrix, and speckle noise suppression filtering is performed on the SAR image to be enhanced, which can better retain the texture details in the image, thereby significantly improving the clarity of the image after quality enhancement.

[0038] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A schematic diagram of a process of an image quality enhancement method based on speckle noise suppression provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of similarity between image blocks provided by an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a SAR image to be enhanced provided by an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of a target SAR image provided by an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the structure of an image quality enhancement device based on speckle noise suppression provided by an embodiment of the present invention;

[0046] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] At present, the traditional non-local mean filtering algorithm may have the problem of image blurring due to the loss of image details. Based on this, the present invention implements an image quality enhancement method, device and equipment based on speckle noise suppression, which can better preserve the texture details in the image, thereby significantly improving the clarity of the image after quality enhancement.

[0049] To facilitate understanding of this embodiment, firstly, a method for enhancing image quality based on speckle noise suppression disclosed in an embodiment of the present invention is described in detail. Figure 1 The flowchart of a method for image quality enhancement based on speckle noise suppression is shown, and the method mainly includes the following steps S102 to S108:

[0050] Step S102: acquiring a SAR image to be enhanced.

[0051] Step S104: extracting a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced based on the position of each pixel to be enhanced in the SAR image to be enhanced.

[0052] Part or all of the pixels in the SAR image to be enhanced may be used as pixels to be enhanced, and the gray value matrix may be a matrix composed of the gray values ​​of each pixel in a neighborhood region centered on the pixel to be enhanced. In one embodiment, the neighborhood region centered on the pixel to be enhanced may be determined according to the position of the pixel to be enhanced, and then the gray values ​​of the pixels in the neighborhood region may be extracted to construct a gray value matrix corresponding to the pixel to be enhanced.

[0053] Step S106: determining similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced according to the gray value matrix.

[0054] Among them, similar pixels can be understood as pixels with similar structures to the pixels to be enhanced. In one embodiment, the gray value matrix corresponding to the pixel to be enhanced and other pixels can be used to calculate the distance between the two. The distance between the pixel to be enhanced and other pixels is negatively correlated with the similarity between the pixel to be enhanced and other pixels, that is, when the distance value approaches zero, it means that the similarity between the pixel to be enhanced and other pixels is very high, and then the distance is used to screen out similar pixels corresponding to the pixel to be enhanced from the SAR image to be enhanced.

[0055] Step S108, using similar pixels corresponding to each pixel to be enhanced, perform speckle noise suppression filtering on the SAR image to be enhanced to obtain a target SAR image.

[0056] In one implementation, a weight value may be determined according to the distance between the pixel to be enhanced and other pixel points, and the similarity is positively correlated with the weight value, that is, the distance is negatively correlated with the weight value. The weight value is then used to perform weighted averaging on the grayscale values ​​corresponding to similar pixel points to obtain the enhanced grayscale value of the pixel to be enhanced. The enhanced grayscale value is used to replace the corresponding grayscale value in the SAR image to be enhanced, so that speckle noise suppression filtering can be performed on the SAR image to be enhanced, thereby obtaining the target SAR image.

[0057] The image quality enhancement method based on speckle noise suppression provided by the embodiment of the present invention extracts the corresponding gray value matrix according to the position of the pixel point to be enhanced, fully considers the pattern redundancy of the SAR image to be enhanced, and on this basis uses the gray value matrix to determine the similar pixel points corresponding to the pixel point to be enhanced, and performs speckle noise suppression filtering on the SAR image to be enhanced, which can better retain the texture details in the image, thereby significantly improving the clarity of the image after quality enhancement.

[0058] For ease of understanding, an embodiment of the present invention provides a specific implementation of an image quality enhancement method based on speckle noise suppression.

[0059] For the aforementioned step S104, the embodiment of the present invention provides an implementation method of extracting a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced based on the position of each pixel to be enhanced in the SAR image to be enhanced, see the following steps A1 to A2:

[0060] Step A1: for each pixel to be enhanced in the SAR image to be enhanced, taking the position of the pixel to be enhanced as the center, extracting the adjacent area corresponding to the pixel to be enhanced from the SAR image to be enhanced based on a preset window size.

[0061] In one example, assuming that all pixels in the SAR image to be enhanced are pixels to be enhanced, for any pixel i, all pixels in a window N of size W×W can be taken out with pixel i as the center to obtain the adjacent area corresponding to pixel i. Optionally, W can be 3 or 5, such as taking out all pixels in a window N of size 3×3 or 5×5 to obtain the adjacent area corresponding to pixel i.

[0062] Step A2: constructing a gray value matrix corresponding to the pixel to be enhanced based on the gray values ​​corresponding to the pixel in the adjacent area.

[0063] In one example, the grayscale values ​​of all pixels in the adjacent area are extracted to construct a grayscale value matrix N(i) corresponding to pixel i.

[0064] For the aforementioned step S106, the embodiment of the present invention provides an implementation method for determining similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced according to the gray value matrix, see the following steps B1 to B2:

[0065] Step B1: for each pixel to be enhanced in the SAR image to be enhanced, determine the distance between the pixel to be enhanced and other pixels in the SAR image to be enhanced according to the gray value matrix.

[0066] In the specific implementation, please refer to the following steps B1-1 to B1-3:

[0067] Step B1-1, using the gray value matrix corresponding to the pixel to be enhanced as a first gray value matrix, and using the gray value matrices corresponding to other pixels in the SAR image to be enhanced as a second gray value matrix;

[0068] In one example, the first gray value matrix corresponding to the pixel i to be enhanced is recorded as N(i), the other pixels except the pixel i to be enhanced are recorded as pixel j, and the second gray value matrix corresponding to pixel j is recorded as N(j). In a specific implementation, the first gray value matrix N(i) and the second gray value matrix N(j) are adjacent areas centered on pixel i and pixel j, respectively, which determine the similarity between pixel i and pixel j.

[0069] Step B1-2, determining the covariance matrix between the first gray value matrix and the second gray value matrix.

[0070] The covariance matrix is ​​used to describe the correlation between the first gray value matrix N(i) and the second gray value matrix N(j). In one example, the covariance matrix is ​​denoted as S.

[0071] Step B1-3, determining the distance between the pixel to be enhanced and other pixels according to the first gray value matrix, the second gray value matrix and the inverse matrix of the covariance matrix.

[0072] Optionally, Gaussian weighted Euclidean distance may be used to represent the similarity between pixel point i and pixel point j.

[0073] In one example, the Gaussian weighted Euclidean distance between pixel i and pixel j can be calculated according to the following (1) to (3): (1) determine the matrix difference between the first gray value matrix N(i) and the second gray value matrix N(j); (2) calculate the transpose of the matrix difference, the inverse matrix S of the covariance matrix and the matrix product between the matrix difference; (3) use the square root of the matrix product as the distance between the pixel to be enhanced and the other pixel points.

[0074] Specifically, the Gaussian weighted Euclidean distance d(i, j) can be calculated according to the following formula:

[0075]

[0076] Wherein, d(i, j) is the Gaussian weighted Euclidean distance between the first gray value matrix N(i) and the second gray value matrix N(j). N(i) and N(j) are gray value matrices centered at pixel i and pixel j, respectively. -1 It is the inverse matrix of the covariance matrix. The covariance matrix S describes the correlation between the first gray value matrix N(i) and the second gray value matrix N(j).

[0077] Gaussian weighted Euclidean distance introduces the covariance matrix S, so that the distance calculation not only considers the difference between the first gray value matrix N(i) and the second gray value matrix N(j), but also considers the correlation between them. This is very useful for processing high-dimensional data, especially when there is correlation between data features.

[0078] Step B2: If the distance between the pixel to be enhanced and other pixels is less than a preset threshold, the other pixels are determined as similar pixels corresponding to the pixel to be enhanced.

[0079] In one example, when the Gaussian weighted Euclidean distance d(i, j) approaches zero, it means that the similarity between two pixels i and j is very high, so the pixel j with a distance less than a preset threshold can be used as a similar pixel corresponding to the pixel i.

[0080] For the aforementioned step S108, the embodiment of the present invention further provides an implementation method of performing speckle noise suppression filtering on the to-be-enhanced SAR image using similar pixels corresponding to each to-be-enhanced pixel point to obtain a target SAR image. The embodiment of the present invention uses a non-local image block processing algorithm to perform speckle noise suppression filtering on the to-be-enhanced SAR image. For details, refer to the following steps C1 to C3:

[0081] Step C1, determining a weight value between the pixel to be enhanced and the similar pixel according to the distance between the pixel to be enhanced and the similar pixel, wherein the weight value is negatively correlated with the distance.

[0082] The None Local Method algorithm stipulates that for a SAR image to be enhanced, pixels with stronger similarity contribute more to the entire filtering process, so their weights should also be larger, and vice versa. Figure 2 A schematic diagram of similarity between image blocks is shown in Figure 2 It can be seen that the structure of the pixel at B is similar to that of the pixels at A and C, and is more similar than that of the pixel at D. Therefore, the weight value w(B,A) and the weight value w(B,C) are both greater than the weight value w(B,D).

[0083] In the specific implementation, please refer to the following steps C1-1 to C1-2:

[0084] Step C1-1, according to the gray value standard deviation and the number of pixels corresponding to similar pixels, determine the smoothing parameter in the weight function. The smoothing parameter is used to control the decay speed of the weight function, and the weight function is the ratio of the kernel function to the normalization function.

[0085] In one example, the expression of the weight function w(i,j) is as follows:

[0086]

[0087] Among them, z(i) is the normalization function, f k (d(i,j)) is the kernel function. The weight function w(i,j) is determined by the similarity between pixel i and pixel j, and the weight function satisfies the following conditions: 0≤w(i,j)≤1,∑ j w(i,j)=1.

[0088] Furthermore, the normalization function z(i) can use the minimum-maximum normalization (Min-MaxNormalization) to linearly map the data to the range of [0,1]. The expression of the minimum-maximum normalization is as follows:

[0089]

[0090] Among them, X is the gray value of pixel i, X max is the maximum gray value in the SAR image to be enhanced, X min is the minimum gray value in the SAR image to be enhanced.

[0091] Furthermore, the kernel function f k The expression of (d(i,j)) is as follows:

[0092]

[0093] Among them, is the smoothing parameter of the exponential function, which controls the decay speed of the weight function. The smoothing parameter can be calculated according to the following methods 1 to 4:

[0094] Method 1: Silverman's Rule of Thumb:

[0095]

[0096] Among them, standard deviation is the standard deviation of grayscale value, IQR (Interquartile Range) is the interquartile range, and n is the sample size (that is, the number of pixels).

[0097] Method 2: Scott's Rule:

[0098]

[0099] Among them, standard deviation is the standard deviation of grayscale value, and n is the sample size (that is, the number of pixels).

[0100] Method 3: Kernel Density Estimation:

[0101] Kernel density estimation itself includes the choice of bandwidth. Common kernel functions include Gaussian kernel, rectangular kernel, triangular kernel, etc., and the bandwidth is usually determined by methods such as cross-validation.

[0102] Method 4: Median Absolute Deviation (MAD):

[0103] h = c × MAD;

[0104] Among them, the constant c can be selected according to the specific problem and data distribution.

[0105] Step C1-2: Substitute the smoothing parameter and the distance between the pixel to be enhanced and the similar pixel into the weight function to obtain the weight value between the pixel to be enhanced and the similar pixel.

[0106] In one example, the smoothing parameter h and the Gaussian weighted Euclidean distance d(i, j) are substituted into the kernel function, and then the calculation result of the kernel function is substituted into the weight function w(i, j), and the weight value between the pixel to be enhanced and the similar pixel can be obtained.

[0107] Step C2: Based on the weight value, weighted average is performed on the grayscale values ​​corresponding to similar pixels to obtain the target grayscale value corresponding to the pixel to be enhanced.

[0108] In one example, the target grayscale value corresponding to the pixel i to be enhanced can be determined as follows:

[0109] Assume that the discretized noisy SAR image to be enhanced is p = {p(i)|i∈I}, where I is the image domain. The gray value of any pixel i in the image is obtained by weighted average of all pixels with similar structures in the image. That is:

[0110]

[0111] Among them, w(i,j) is the weight function, which represents the weight value between pixel i and pixel j, and p(j) is the gray value of pixel j. is the target grayscale value corresponding to the pixel i to be enhanced.

[0112] Step C3, assigning the target grayscale value corresponding to the pixel to be enhanced to the SAR image to be enhanced, so as to suppress the speckle noise of the SAR image to be enhanced and obtain the target SAR image.

[0113] Exemplarily, the processing effect of speckle noise suppression filtering is as follows Figure 3 and Figure 4 As shown, Figure 3 is a schematic diagram of a SAR image to be enhanced. Figure 4 The schematic diagram of a target SAR image shows that the method used can effectively suppress coherent speckle noise. The embodiment of the present invention uses a non-local image block processing method, fully considers the pattern redundancy of the SAR image, can well preserve texture detail information, and improves the effect of image quality enhancement.

[0114] Based on the above-mentioned embodiments, the present invention provides an image quality enhancement device based on speckle noise suppression. Figure 5 The structure diagram of an image quality enhancement device based on speckle noise suppression is shown, and the device mainly includes the following parts:

[0115] An image acquisition module 502 is used to acquire a SAR image to be enhanced;

[0116] A grayscale extraction module 504 is used to extract a grayscale value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced based on the position of each pixel to be enhanced in the SAR image to be enhanced;

[0117] A similar point determination module 506 is used to determine similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced according to the gray value matrix;

[0118] The noise suppression module 508 is used to perform speckle noise suppression filtering on the SAR image to be enhanced by using similar pixels corresponding to each pixel to be enhanced to obtain a target SAR image.

[0119] The image quality enhancement device based on speckle noise suppression provided by the embodiment of the present invention extracts the corresponding gray value matrix according to the position of the pixel point to be enhanced, fully considers the pattern redundancy of the SAR image to be enhanced, and on this basis uses the gray value matrix to determine the similar pixel points corresponding to the pixel point to be enhanced, and performs speckle noise suppression filtering on the SAR image to be enhanced, which can better retain the texture details in the image, thereby significantly improving the clarity of the image after quality enhancement.

[0120] In one embodiment, the grayscale extraction module 504 is further used to:

[0121] For each pixel to be enhanced in the SAR image to be enhanced, taking the position of the pixel to be enhanced as the center, extracting the adjacent area corresponding to the pixel to be enhanced from the SAR image to be enhanced based on a preset window size;

[0122] Based on the grayscale values ​​corresponding to the pixels in the adjacent area, a grayscale value matrix corresponding to the pixel to be enhanced is constructed.

[0123] In one implementation, the similarity determination module 506 is further configured to:

[0124] For each pixel to be enhanced in the SAR image to be enhanced, determining the distance between the pixel to be enhanced and other pixels in the SAR image to be enhanced according to the gray value matrix;

[0125] If the distance between the pixel to be enhanced and other pixels is less than a preset threshold, the other pixels are determined as similar pixels corresponding to the pixel to be enhanced.

[0126] In one implementation, the similarity determination module 506 is further configured to:

[0127] Using the gray value matrix corresponding to the pixel to be enhanced as a first gray value matrix, and using the gray value matrix corresponding to other pixel points in the SAR image to be enhanced as a second gray value matrix;

[0128] Determine a covariance matrix between the first gray value matrix and the second gray value matrix; wherein the covariance matrix is ​​used to describe the correlation between the first gray value matrix and the second gray value matrix;

[0129] The distance between the pixel to be enhanced and other pixel points is determined according to the first gray value matrix, the second gray value matrix and the inverse matrix of the covariance matrix.

[0130] In one implementation, the similarity determination module 506 is further configured to:

[0131] Determine a matrix difference between the first gray value matrix and the second gray value matrix;

[0132] Compute the transpose of the matrix difference, the inverse of the covariance matrix, and the matrix product between the matrix difference;

[0133] The square root of the matrix product is used as the distance between the pixel to be enhanced and other pixels.

[0134] In one implementation, the noise suppression module 508 is further configured to:

[0135] Determine the weight value between the pixel to be enhanced and the similar pixel according to the distance between the pixel to be enhanced and the similar pixel; wherein the weight value is negatively correlated with the distance;

[0136] Based on the weight value, the grayscale values ​​corresponding to similar pixels are weighted averaged to obtain the target grayscale value corresponding to the pixel to be enhanced;

[0137] The target grayscale value corresponding to the pixel to be enhanced is assigned to the SAR image to be enhanced, so as to suppress the speckle noise of the SAR image to be enhanced and obtain the target SAR image.

[0138] In one implementation, the noise suppression module 508 is further configured to:

[0139] According to the gray value standard deviation and the number of pixels corresponding to similar pixels, the smoothing parameter in the weight function is determined; wherein the smoothing parameter is used to control the decay speed of the weight function, and the weight function is the ratio of the kernel function to the normalization function;

[0140] The smoothing parameter and the distance between the pixel to be enhanced and the similar pixel are substituted into the weight function to obtain the weight value between the pixel to be enhanced and the similar pixel.

[0141] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0142] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0143] Figure 6 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0144] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0145] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0146] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0147] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0148] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0149] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0150] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for enhancing image quality based on speckle noise suppression, characterized in that: include: Acquire the SAR image to be enhanced; Based on the position of each pixel to be enhanced in the SAR image to be enhanced, extracting a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced; Determine, according to the gray value matrix, similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced; Using the similar pixel points corresponding to each pixel point to be enhanced, performing speckle noise suppression filtering on the SAR image to be enhanced to obtain a target SAR image; The step of extracting a gray value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced based on the position of each pixel to be enhanced in the SAR image to be enhanced comprises: for each pixel to be enhanced in the SAR image to be enhanced, taking the position of the pixel to be enhanced as the center, extracting an adjacent area corresponding to the pixel to be enhanced from the SAR image to be enhanced based on a preset window size; constructing a gray value matrix corresponding to the pixel to be enhanced based on the gray values ​​corresponding to the pixels in the adjacent area; The step of determining similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced according to the gray value matrix comprises: for each pixel point to be enhanced in the SAR image to be enhanced, determining the distance between the pixel point to be enhanced and other pixel points in the SAR image to be enhanced according to the gray value matrix; if the distance between the pixel point to be enhanced and the other pixel points is less than a preset threshold, determining the other pixel points as similar pixel points corresponding to the pixel point to be enhanced; The step of performing speckle noise suppression filtering on the SAR image to be enhanced by using the similar pixel points corresponding to each pixel point to be enhanced to obtain a target SAR image comprises: determining a weight value between the pixel point to be enhanced and the similar pixel points according to the distance between the pixel point to be enhanced and the similar pixel points; wherein the weight value is negatively correlated with the distance; performing weighted averaging on the grayscale values ​​corresponding to the similar pixel points based on the weight value to obtain a target grayscale value corresponding to the pixel point to be enhanced; and assigning the target grayscale value corresponding to the pixel point to be enhanced to the SAR image to be enhanced, so as to suppress speckle noise on the SAR image to be enhanced and obtain a target SAR image.

2. The image quality enhancement method based on speckle noise suppression according to claim 1, characterized in that: The step of determining the distance between the pixel to be enhanced and other pixel points in the SAR image to be enhanced according to the gray value matrix comprises: Using the gray value matrix corresponding to the pixel to be enhanced as a first gray value matrix, and using the gray value matrix corresponding to other pixel points in the SAR image to be enhanced as a second gray value matrix; Determine a covariance matrix between the first gray value matrix and the second gray value matrix; wherein the covariance matrix is ​​used to describe the correlation between the first gray value matrix and the second gray value matrix; The distance between the pixel to be enhanced and the other pixel points is determined according to the first gray value matrix, the second gray value matrix and the inverse matrix of the covariance matrix.

3. The image quality enhancement method based on speckle noise suppression according to claim 2, characterized in that: The step of determining the distance between the pixel to be enhanced and the other pixel points according to the first gray value matrix, the second gray value matrix and the inverse matrix of the covariance matrix comprises: Determine a matrix difference between the first gray value matrix and the second gray value matrix; Calculating a matrix product between the transpose of the matrix difference, the inverse of the covariance matrix, and the matrix difference; The square root value of the matrix product is used as the distance between the pixel to be enhanced and the other pixel points.

4. The image quality enhancement method based on speckle noise suppression according to claim 1, characterized in that: The step of determining a weight value between the pixel to be enhanced and the similar pixel according to the distance between the pixel to be enhanced and the similar pixel comprises: Determine a smoothing parameter in a weight function according to the gray value standard deviation and the number of pixels corresponding to the similar pixels; wherein the smoothing parameter is used to control the decay speed of the weight function, and the weight function is the ratio of a kernel function to a normalization function; The smoothing parameter and the distance between the pixel to be enhanced and the similar pixel are substituted into the weight function to obtain a weight value between the pixel to be enhanced and the similar pixel.

5. An image quality enhancement device based on speckle noise suppression, characterized in that: include: An image acquisition module, used for acquiring the SAR image to be enhanced; A grayscale extraction module, configured to extract a grayscale value matrix corresponding to each pixel to be enhanced in the SAR image to be enhanced based on the position of each pixel to be enhanced in the SAR image to be enhanced; A similar point determination module, used for determining similar pixel points corresponding to each pixel point to be enhanced in the SAR image to be enhanced according to the gray value matrix; A noise suppression module, configured to perform speckle noise suppression filtering on the SAR image to be enhanced by using the similar pixel points corresponding to each pixel point to be enhanced, so as to obtain a target SAR image; The grayscale extraction module is specifically used to: for each pixel to be enhanced in the SAR image to be enhanced, extract the adjacent area corresponding to the pixel to be enhanced from the SAR image to be enhanced based on a preset window size, with the position of the pixel to be enhanced as the center; and construct a grayscale value matrix corresponding to the pixel to be enhanced based on the grayscale values ​​corresponding to the pixels in the adjacent area; The similarity point determination module is specifically used to: for each pixel point to be enhanced in the SAR image to be enhanced, determine the distance between the pixel point to be enhanced and other pixel points in the SAR image to be enhanced according to the gray value matrix; If the distance between the pixel to be enhanced and the other pixel points is less than a preset threshold, the other pixel points are determined as similar pixel points corresponding to the pixel to be enhanced; The noise suppression module is specifically used to: determine the weight value between the pixel to be enhanced and the similar pixel according to the distance between the pixel to be enhanced and the similar pixel; wherein the weight value is negatively correlated with the distance; based on the weight value, perform weighted averaging on the grayscale values ​​corresponding to the similar pixel points to obtain the target grayscale value corresponding to the pixel to be enhanced; assign the target grayscale value corresponding to the pixel to be enhanced to the SAR image to be enhanced, so as to suppress speckle noise on the SAR image to be enhanced and obtain the target SAR image.

6. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 4.

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