SAR (Synthetic Aperture Radar) image quality evaluation method for edge structure definition

Abstract

The invention relates to the technical field of image quality evaluation, in particular to an SAR image quality evaluation method for edge structure definition, which comprises the following steps: setting a brightness threshold, separating highlight spots in an SAR image, and calculating the highlight spot aggregation density of the SAR image; performing edge extraction on the SAR image by using two edge extraction methods, and calculating the primary edge extraction similarity of two edge extraction results; if the primary edge extraction similarity exceeds a set threshold value, carrying out secondary fuzzy processing on the image and carrying out edge extraction again, and calculating secondary edge extraction similarity; and calculating a comprehensive quality evaluation value of the SAR image according to a relation selection expression of the primary edge extraction similarity, a set threshold value and the secondary edge extraction similarity. According to the method, through combination of highlight spot aggregation density analysis and multi-stage edge similarity dynamic evaluation, the adaptability of quality evaluation to a complex scattering scene and the objective characterization capability of edge definition are remarkably improved.

Description

Background Art

[0002] Synthetic Aperture Radar (SAR), as an active microwave remote sensing imaging technology, plays an irreplaceable role in fields such as geological exploration and disaster monitoring. Due to the particularity of the SAR imaging mechanism, its image quality is comprehensively affected by sensor parameters, imaging conditions, and post-processing algorithms, showing characteristics such as uneven resolution distribution, significant multiplicative noise, and blurred edge structures. With the exponential growth of SAR data acquisition capabilities, how to establish an image quality evaluation system that matches the human interpretation requirements has become a key issue in improving the target recognition efficiency and data application value.

[0003] Existing no-reference image quality evaluation methods mainly focus on the characteristics of visible light images. Among them, methods based on natural scene statistical features achieve quality assessment by constructing parameter models such as image texture and gradient distribution to simulate the human visual perception characteristics; another type of method quantifies the degree of image degradation by detecting image blur and noise intensity. Such techniques have achieved certain results in visible light image evaluation, especially forming a relatively mature evaluation framework in Gaussian noise suppression and sharpness detection. In addition, the evaluation strategy based on second-order blur provides a new idea for measuring edge preservation ability by analyzing the response differences of image edge regions before and after blur.

[0004] However, existing image quality evaluation methods have significant limitations in SAR image quality evaluation. First, the multiplicative noise characteristics of SAR images are essentially different from the additive noise model of visible light, resulting in difficulty for methods based on noise detection to accurately quantify the impact of speckle noise on edge structures. Second, the non-uniformity of SAR image resolution in spatial distribution makes it difficult for traditional statistical models to construct universal feature expressions. More importantly, existing edge blur evaluation methods rely on the accurate distinction between uniform regions and edge regions, and the widespread weak texture regions and complex scattering characteristics in SAR images lead to easy misjudgment of such methods in edge response analysis, unable to effectively reflect the coupled influence of resolution and blur on edge sharpness. Summary of the Invention

[0005] Aiming at the technical problem that existing SAR image quality evaluation methods are limited by the migration failure of visible light models, the coupled interference of noise and blur, and the difficulty in identifying edge regions, and it is difficult to accurately quantify the edge sharpness under no-reference conditions, this application provides a SAR image quality evaluation method for edge structure sharpness. By combining the analysis of high-brightness speckle aggregation density and the dynamic evaluation of multi-stage edge similarity, it solves the problems of difficult quantification of multiplicative noise interference, invalid modeling of non-uniform resolution distribution, and misjudgment of edge blur in weak texture regions in SAR images, and significantly improves the adaptability of quality evaluation to complex scattering scenes and the objective characterization ability of edge sharpness.

[0006] In a first aspect, the present application provides a SAR image quality evaluation method for edge structure clarity, including the following steps: S1. Set a brightness threshold to separate the highlight spots in the SAR image, and calculate the aggregation density of the highlight spots in the SAR image ; S2. Use two edge extraction methods to perform edge extraction on the SAR image respectively, and calculate the initial edge extraction similarity of the two edge extraction results ; S3. Compare with the set threshold t . If > t , then perform secondary blurring on the SAR image, and perform the same processing as in step S2 on the secondary blurred image to calculate the secondary edge extraction similarity ; S4. Calculate the comprehensive quality evaluation value of the SAR image according to the following conditions: When ≤ t , or > t and < , the expression of the comprehensive image quality evaluation value is: ; When > t , and ≥ , the expression of the comprehensive image quality evaluation value is:

[0007] In the formula, , is a preset weighting coefficient.

[0008] Furthermore, it should be noted that step S1 specifically includes: S101. Normalize the grayscale of the SAR image, set the brightness threshold , and perform binary processing on the grayscale-normalized SAR image. The formula is:

[0009] In the formula, is the pixel value after binary separation of point ; is the normalized pixel value of point ; S102. Set the scale to Traverse the binarized SAR image in the window, and record the number of pixels with pixel value 1 in each pixel window , calculate the bright spot density in the window , the bright spot density in the k-th window is expressed as: ; S103. Count all non-zero number of , calculate : .

[0010] It should be further noted that in step S2, the LoG operator and the Canny operator are respectively used for edge extraction.

[0011] It should be further noted that in step S2, The calculation formula of is:

[0012] In the formula, , respectively represent the edge extraction images obtained by using two edge extraction methods; represents at the point in the figure point pixel value of; represents at the point in the figure point pixel value of; p is the number of columns of the edge extraction image, q is the number of rows of the edge extraction image.

[0013] Further preferably, in step S3, the threshold t = 0.5.

[0014] It should be further noted that in step S3, the secondary blur uses Gaussian blur or defocus blur.

[0015] It should be further noted that the transfer function of defocus blur is:

[0016] In the formula, represents the first kind of Bessel function of the first order; R is the defocus radius; and are frequency domain coordinates, u 、 vrespectively represent the frequency components in the horizontal and vertical directions.

[0017] It should be further noted that the transfer function of Gaussian blur is expressed as:

[0018] In the formula, is the standard deviation of the Gaussian kernel.

[0019] In a second aspect, the present application provides a SAR image quality evaluation system for edge structure clarity, which is used to implement the above SAR image quality evaluation method, including: A SAR image input module, which is used to input a SAR image; An image preprocessing module, which is used to set a brightness threshold and separate the high-brightness spots in the SAR image; A high-brightness spot density calculation module, which is used to calculate the aggregation density of the high-brightness spots in the SAR image; An edge extraction module, which is used to perform edge extraction on the SAR image using two edge extraction methods respectively; An edge similarity calculation module, which is used to calculate the primary edge extraction similarity of the two edge extraction results and calculate the secondary edge extraction similarity; A secondary blur processing module, which is used to compare with a set threshold t. If >t, then perform secondary blur on the SAR image; A condition judgment and evaluation module, which is used to calculate the comprehensive quality evaluation value of the SAR image according to preset conditions.

[0020] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor is used to implement the steps of the above SAR image quality evaluation method for edge structure clarity when executing the computer program.

[0021] In a fourth aspect, the present application provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above SAR image quality evaluation method for edge structure clarity are implemented.

[0022] From the above technical solutions, it can be seen that the present application has the following advantages: 1. By calculating the aggregation density of high-brightness spots and dynamically adjusting in combination with edge extraction similarity, the present application solves the problem of inaccurate quantization of the influence of edge structures caused by differences in the characteristics of multiplicative noise in existing methods, realizes the accurate evaluation of the coupling effect of speckle noise and edge clarity, and overcomes the limitation of the failure of the visible light noise model in SAR images.

[0023] 2. The present application solves the problem of the difficulty in modeling the non-uniformity of SAR image resolution by traditional statistical models by fusing two edge extraction results and introducing threshold adaptive judgment, realizes the robust expression of edge structure features with uneven spatial distribution, and improves the universality of the quality evaluation model in complex scenarios.

[0024] 3. The present application solves the problem of misjudgment of edge blurring caused by complex scattering characteristics in weak texture regions through secondary fuzzy processing and edge similarity comparative analysis, realizes the effective distinction between the clarity of real edge structures and the degree of blurring degradation, and avoids the sensitivity dependence on pseudo-edges or noise interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the present application, the drawings required for description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0026] Figure 1 It is a flowchart of a SAR image quality evaluation method for edge structure clarity in an embodiment of the present application.

[0027] Figure 2 It is a high-resolution SAR image after gray normalization in an embodiment of the present application.

[0028] Figure 3 It is a medium-resolution SAR image after gray normalization in an embodiment of the present application.

[0029] Figure 4 It is a low-resolution SAR image after gray normalization in an embodiment of the present application.

[0030] Figure 5 It is a graph showing the change of edge extraction similarity of three images with the defocus radius in an embodiment of the present application.

[0031] Figure 6 It is a comparison graph of objective-subjective evaluation of SAR image quality in an embodiment of the present application.

[0032] Figure 7 It is a comparison graph of objective-average subjective evaluation of SAR image quality in an embodiment of the present application.

[0033] Figure 8 It is a schematic block diagram of a SAR image quality evaluation system for edge structure clarity in an embodiment of the present application.

[0034] Figure 9 It is a schematic diagram of the hardware structure of an electronic device in an embodiment of the present application. Detailed implementation manners

[0035] In order to make the application purpose, features, and advantages of the present application more obvious and understandable, the technical solutions protected by the present application will be clearly and completely described below by using specific embodiments and accompanying drawings. Obviously, the embodiments described below are only a part of the embodiments of the present application, rather than all embodiments. Based on the embodiments in this patent, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this patent.

[0036] The SAR image quality evaluation method for edge structure clarity involved in the present application mainly aims at the field of image quality evaluation technology. By calculating the aggregation density of bright spots and dynamically adjusting in combination with the edge extraction similarity, it solves the problem of inaccurate quantification of the influence of edge structures caused by differences in multiplicative noise characteristics in existing methods, realizes the accurate evaluation of the coupling effect of speckle noise and edge clarity, and overcomes the failure limitation of the visible light noise model in SAR images; by fusing the results of two edge extractions and introducing threshold adaptive judgment, it solves the problem of difficult modeling of the non-uniformity of SAR image resolution by traditional statistical models, realizes the robust expression of edge structure features with uneven spatial distribution, and improves the universality of the quality evaluation model in complex scenarios; through secondary fuzzy processing and edge similarity comparative analysis, it solves the problem of misjudgment of edge blurring caused by complex scattering characteristics in weak texture regions, realizes the effective distinction between the clarity of real edge structures and the degree of blurring degradation, and avoids the sensitivity dependence on pseudo-edges or noise interference.

[0037] The SAR image quality evaluation method for edge structure clarity involved in the present application mainly aims at the technical problem that the existing SAR image quality evaluation methods are limited by the migration failure of the visible light model, the coupling interference of noise and blurring, and the difficulty in identifying edge regions, and it is difficult to accurately quantify the edge clarity under the condition of no reference.

[0038] The SAR image quality evaluation method for edge structure clarity involved in the present application will be described in detail below. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details.

[0039] In the SAR image quality evaluation method for edge structure clarity involved in this application, the term "including" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations. The terms "including", "comprising", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0040] For the convenience of clearly describing the technical solutions of this application, terms such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and roles. Those skilled in the art can understand that the terms such as "first" and "second" do not limit the quantity and execution order, and the terms such as "first" and "second" do not necessarily limit to being different.

[0041] The statements such as "an embodiment" or "some embodiments" described in this application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of this application. Thus, the statements such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other additional embodiments" and the like that appear in different places in this application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0042] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope protected by this application.

[0043] The SAR image quality evaluation method for edge structure clarity provided by the embodiments of this application is executed by a computer device. Correspondingly, the SAR image quality evaluation system for edge structure clarity runs in the computer device.

[0044] The following are some noun explanations in this solution to facilitate a better understanding of this solution: SAR image: The SAR image (Synthetic Aperture Radar Image) is a high-resolution two-dimensional image generated by an active microwave remote sensing detection of the earth's surface or a target by a synthetic aperture radar system. Its core principle is to use the relative motion between the radar antenna and the target to form a "synthetic aperture", combined with pulse compression technology and phase coherence processing, to significantly improve the azimuth resolution of the radar, so as to obtain high-precision ground feature information.

[0045] Highlighted Spots: Highlighted spots are dot-like or small-area bright regions in an image where the local brightness is significantly higher than the surrounding area. They are usually caused by strong reflections, highlights, noise, or specific target features (such as metal reflections, calcification points in medical images). Their core feature is that the pixel values show a sharp peak in the local area, and they appear as circular, elliptical, or irregular bright spots in shape. Edge Extraction: Edge extraction is a fundamental technique in digital image processing for identifying the contours of objects or the boundaries of scenes. Its principle is based on capturing significant mutations in pixel values in the image, such as changes in brightness or color at the junction of an object and the background. By calculating the gradient (i.e., the rate of change of pixel values), these edge regions are located. Common methods include the Sobel operator, Prewitt operator based on gradients, and more complex operators such as the LoG operator and Canny edge detection operator.

[0046] Figure 1 It is a flowchart of a SAR image quality evaluation method for edge structure clarity according to an embodiment of the present application. Among them, Figure 1 The execution subject can be a SAR image quality evaluation system for edge structure clarity. According to different requirements, the order of the steps in this flowchart can be changed, and some can be omitted.

[0047] As Figure 1 shown, the SAR image quality evaluation method for edge structure clarity includes: Step S1, set a brightness threshold, separate the highlighted spots in the SAR image, and calculate the aggregation density of the highlighted spots in the SAR image .

[0048] By setting the brightness threshold and separating the highlighted spots, the accurate identification and density calculation of the highlighted regions in the image are achieved, thereby effectively evaluating the distribution of speckle noise in the image and providing a key feature basis for subsequent quality evaluation.

[0049] In some specific embodiments, step S1 includes: S101. Normalize the grayscale of the SAR image, set a brightness threshold , and perform binary processing on the grayscale-normalized SAR image. The formula is:

[0050] In the formula, is the pixel value after binary separation of point ; is the normalized pixel value of point ; S102. Set the scale to Traverse the binarized SAR image in the window, and record the number of pixels with pixel value 1 in each pixel window , calculate the bright spot density within the window , the bright spot density within the k-th window is expressed as: ; S103. Count all non-zero number of , calculate : .

[0051] Through gray normalization and brightness threshold binarization processing, accurate segmentation of the high-brightness area is achieved, which can eliminate the influence of illumination differences and enhance the statistical stability of speckle noise; by sliding the window to count the local bright spot density and calculate the mean value, global quantification of the spatial distribution of the high-brightness area is achieved, which can avoid the interference of local outliers to improve the reliability of density features.

[0052] Step S2, use two edge extraction methods to extract edges from the SAR image respectively, and calculate the initial edge extraction similarity of the two edge extraction results .

[0053] By using two edge extraction methods and calculating the initial edge extraction similarity, quantitative analysis of the consistency of the image edge structure is achieved, which can integrate the advantages of different algorithms to more comprehensively reflect the edge sharpness.

[0054] In some specific embodiments, in step S2, the LoG operator and the Canny operator are used for edge extraction respectively.

[0055] Among them, the LoG operator (Laplacian of Gaussian) combines Gaussian filtering and the Laplacian operator to detect edges through the second derivative. Its core idea is to first perform Gaussian smoothing filtering on the image to suppress noise, then calculate the Laplacian operator (second derivative), and finally find the zero-crossing points of the second derivative (that is, the points where the pixel value changes from positive to negative or from negative to positive) as the edge positions; the LoG operator is sensitive to edge responses and can capture the extreme points of intensity changes (such as the center line of the edge) at the same time, but it is also sensitive to noise and depends on the smoothing degree of Gaussian filtering to control noise interference. Its disadvantage is that it may produce double-edge responses and the calculation amount is large; The Canny operator is a multi-stage optimized edge detection algorithm, and its core steps include: (1) Gaussian filtering to smooth the image to reduce noise; (2) Use the Sobel operator to calculate the gradient magnitude and direction; (3) Non-maximum suppression, retaining the local maximum in the gradient direction to refine the edges; (4) Double-threshold hysteresis processing, determining strong edges through a high threshold and connecting weak edges (weak edges are retained only when adjacent to strong edges); The advantage of the Canny operator is that it can effectively suppress noise while maintaining the continuity and accuracy of edges. However, its performance depends on the reasonable selection of threshold parameters and may produce broken edges for complex textures.

[0056] By using the LoG operator and the Canny operator for edge extraction, complementary detection of step-type edges and detail features is achieved, covering edge information at different scales and contrasts to improve the comprehensiveness of similarity calculation.

[0057] In some specific embodiments, The calculation formula of

[0058] In the formula, , respectively represent the edge extraction images obtained by using two edge extraction methods; represents the pixel value at point in Figure ; represents the pixel value at point in Figure ; p is the number of columns of the edge extraction image, q is the number of rows of the edge extraction image.

[0059] This application selects the Edge Preservation Index (EPI) for similarity calculation. EPI calculates the ratio of the sum of the absolute values of the differences in gray levels of adjacent pixels in the horizontal and vertical directions of two images. The closer the EPI value is to 1, the higher the edge similarity.

[0060] By calculating the edge extraction similarity through the normalized gradient magnitude difference, an effective measurement of the consistency of edge position and intensity is achieved, which can eliminate the influence of image size differences to enhance the universality of the evaluation index.

[0061] Step S3, compare with the set threshold t . If > t , then perform secondary blurring on the SAR image and adopt the same processing as in step S2 for the secondary blurred image to calculate the secondary edge extraction similarity .

[0062] By comparing the initial edge extraction similarity with a threshold and selectively performing secondary blurring, the adaptive adjustment of the blurring degree is achieved, and real edges can be distinguished from noise interference to improve the accuracy of quality evaluation.

[0063] In some specific embodiments, the threshold is set t = 0.5.

[0064] In some specific embodiments, Gaussian blurring or defocus blurring is used for the secondary blurring.

[0065] By performing secondary blurring through Gaussian blurring or defocus blurring, the controllable simulation of the image degradation process is achieved, and an adaptation method can be selected according to different noise types to improve the pertinence of the blurring process.

[0066] In some specific embodiments, the transfer function of the defocus blurring is:

[0067] In the formula, represents the first-kind Bessel function of the first order; R is the defocus radius; and are frequency domain coordinates, u , v respectively represent the frequency components in the horizontal and vertical directions.

[0068] Through the defocus blurring transfer function based on the Bessel function, the accurate modeling of the defocus effect of the optical system is achieved, and the real imaging degradation process can be simulated to improve the physical rationality of the quality evaluation.

[0069] In some specific embodiments, the defocus radius R = 5.

[0070] In some specific embodiments, the transfer function of the Gaussian blurring is expressed as:

[0071] In the formula, is the standard deviation of the Gaussian kernel, is the variance of the Gaussian kernel.

[0072] By adjusting the standard deviation parameter through the Gaussian blurring transfer function, the flexible control of the blurring degree is achieved, and different noise intensity scenarios can be adapted to improve the adaptability of the image quality degradation simulation.

[0073] In some specific embodiments, the size of the Gaussian kernel of the Gaussian blurring is 3 * 3, and the variance = 2.

[0074] Step S4, calculate the comprehensive quality evaluation value of the SAR image according to the following conditions: When ≤ t or > t and < , the expression of the comprehensive evaluation value of image quality is: ; When > t and ≥ , the expression of the comprehensive evaluation value of image quality is:

[0075] In the formula, , is a preset weighting coefficient.

[0076] Among them, or The larger the value, the worse the quality of the SAR image; or The smaller the value (approaching 0), the better the quality of the SAR image; The weighting coefficient is used to balance the influence of resolution and blur on edge sharpness. According to the situation of the SAR image set to be evaluated, if the resolution is generally low, the resolution has a greater impact on edge sharpness, takes a value greater than 0.5; if the image resolution is generally high, the blur has a greater impact on edge sharpness, takes a value less than 0.5; for specific operations, two images with good and poor quality can be extracted, and a suitable value can be selected to adjust the quality discrimination.

[0077] By combining the edge similarity and the highlight speckle density to generate a comprehensive evaluation value, a multi-dimensional evaluation of the image noise level and edge sharpness is realized, and the weights can be dynamically adjusted according to different scenarios to improve the robustness of the evaluation results.

[0078] In a specific embodiment, the SAR image quality evaluation method for edge structure sharpness of the present application is used to evaluate the quality of 20 actual SAR images with different scenarios and resolutions. The steps include: Step S1, set the brightness threshold, separate the highlight speckles in the SAR image, and calculate the highlight speckle aggregation density of the SAR image , specifically including: S101. Normalize the grayscale of the SAR image, ; set the brightness threshold =0.8, and perform binary processing on the grayscale-normalized SAR image. The formula is:

[0079] wherein, is the pixel value after binarization separation of point ; is the normalized pixel value of point ; S102. Set the scale to , d = 7, traverse the binarized SAR image with a window, record the number of pixels with pixel value 1 (i.e., bright spots) in each pixel window , calculate the bright spot density in the k-th window, and the bright spot density in the k-th window is expressed as: ; S103. Count the number of all non-zero , calculate : : ; Among them, the larger the binarization threshold , the larger the grid cell scale , and the smaller the high-brightness spot density value; conversely, the larger. To avoid the order of magnitude of the density value from being too small, appropriate sizes of and need to be selected; in this embodiment, is set to take values from {0.7, 0.75, 0.8, 0.85, 0.9, 0.95}, is set to take values from {5, 7, 9}, and calculate the bright spot aggregation density of 30 SAR images. It is found by comparison that when , , the aggregation density values of the high, medium, and low resolution level images are relatively scattered, and three interval values can be defined corresponding to images of different resolution levels:

[0080] Figure 2 is the high-resolution SAR image after gray-scale normalization in this embodiment, and the value is 0.0485; Figure 3 is the medium-resolution SAR image after gray-scale normalization in this embodiment, and the value is 0.1157; Figure 4 is the low-resolution SAR image after gray-scale normalization in this embodiment, value is 0.4272; It can be seen that high-resolution SAR images can show the outlines and even details of individual targets, medium-resolution SAR images can show the outlines of large targets, and the outlines of large targets in low-resolution SAR images are relatively blurred. It can be seen that the edge sharpness of low-resolution SAR images is mainly affected by the resolution, while for medium- and high-resolution SAR images, the edge structure is more affected by noise and blurring.

[0081] Step S2: Use the LoG operator and the Canny operator respectively to perform edge extraction on the SAR image. Both methods can detect fine edges, and they only differ in noise suppression and edge connection. Calculate the initial edge extraction similarity of the two edge extraction results , The closer the value is to 1, the higher the edge similarity. The calculation formula is:

[0082] In the formula, , respectively represent the edge extraction images obtained by using the two edge extraction methods; represents at point in the image the pixel value; represents at point in the image the pixel value; p is the number of columns of the edge extraction image, q is the number of rows of the edge extraction image; The clearer the image edge, the higher the similarity of its edge extraction; the lower the similarity of the image edge extraction, the blurrier the image edge. However, the similarity of edge extraction and the degree of edge sharpness do not have a simple positive correlation. Experiments were conducted on real-shot images of three typical cases, which were to simulate different degrees of blurring for three images. Taking the defocus blurring common in SAR images as an example, the graph of the change in the edge extraction similarity of the three images with the defocus radius is as Figure 5 shown. The first one is a clear optical image with an edge ( =0.9892). As the degree of edge blurring increases, the value continuously decreases, indicating that blurring affects the edge sharpness; but when the blurring reaches a certain degree, it seriously affects edge extraction, the value tends to remain unchanged or even increase; for two SAR images, SAR image 1 and SAR image 2, with similar initial values, SAR image 1 is clearer, and its value is initially 0.7125. As the defocus radius increases, The value becomes smaller, but the defocus radius increases to a certain extent, and the value instead becomes larger; SAR image 2 is relatively blurred, The initial value is 0.7166. As the defocus radius increases, the value becomes larger or tends to remain unchanged.

[0083] Step S3: Compare with the set threshold t If t = 0.5, and if > t , then perform secondary blurring on the SAR image, and perform the same processing as in step S2 on the secondarily blurred image to calculate the similarity of secondary edge extraction ; Among them, the secondary blurring uses defocus blurring, and the transfer function is expressed as:

[0084] In the formula, represents the first-kind Bessel function of the first order; R is the defocus radius, R = 5; and are frequency domain coordinates, u , v respectively represent the frequency components in the horizontal and vertical directions; Step S4: Calculate the comprehensive evaluation value of the quality of the SAR image according to the following conditions: When ≤ t , or > t and < , the expression of the comprehensive evaluation value of the image quality is: ; When > t , and ≥ , the expression of the comprehensive evaluation value of the image quality is:

[0085] In the formula, , is the preset weighting coefficient.

[0086] To verify the effectiveness of the SAR image evaluation method in this embodiment, a comparative analysis of subjective evaluations of real-shot SAR images was conducted. First, the comprehensive image quality evaluation values of 20 real-shot SAR images with different scenarios and resolutions were given using the method of this embodiment, and the SAR images were sorted from best to worst according to the comprehensive image quality evaluation values (from 1 to 20) to obtain the objective evaluation rankings of the SAR image quality; then, 3 professional interpretation personnel were asked to give the subjective evaluation rankings of the SAR image quality from the perspectives of whether the image edges were clear and whether it was conducive to interpretation. The objective evaluation rankings and subjective evaluation rankings of the SAR image quality were summarized and displayed simultaneously in the objective-subjective evaluation comparison chart, as shown in the appendix Figure 6 As shown. Due to different preferences of each person and different parts of the image edges that the human eye focuses on, the rankings of the 20 images by the 3 experts were not exactly the same, nor were they exactly the same as the rankings of the method in this paper.

[0087] The average value of the rankings of each image by the 3 experts was taken to obtain a new average ranking value {1, 2, 3.5, 4, 5, 6, 6.6, 8, 8.3, 8.6, 10.6, 12, 12.3, 14.6, 14.3, 16.6, 17.3, 17.6, 18.3, 20} and the average subjective evaluation ranking. The objective evaluation rankings and average subjective evaluation rankings of the SAR image quality were summarized and displayed simultaneously in the objective-average subjective evaluation comparison chart, as shown in Figure 7 As shown. It can be observed that the objective evaluation rankings and average subjective evaluation rankings are basically consistent, and the correlation degree of the results of the objective evaluation rankings and average subjective evaluation rankings was calculated to be 0.9963. Therefore, the comparative analysis of subjective evaluations shows that the SAR image quality evaluation method for edge structure clarity in this application can effectively conduct objective evaluations of SAR images.

[0088] The following is an embodiment of the SAR image quality evaluation system for edge structure clarity provided by the embodiment of this application. This SAR image quality evaluation system belongs to the same inventive concept as the SAR image quality evaluation method for edge structure clarity in the above-mentioned embodiments. For the details not described in detail in the embodiment of the SAR image quality evaluation system for edge structure clarity, reference can be made to the embodiment of the SAR image quality evaluation method for edge structure clarity.

[0089] As shown in Figure 8 The SAR image quality evaluation system for edge structure clarity includes: An SAR image input module for inputting SAR images; An image preprocessing module for setting a brightness threshold to separate the highlighted spots in the SAR image; A highlighted spot density calculation module for calculating the aggregation density of the highlighted spots in the SAR image; An edge extraction module, which is used to perform edge extraction on the SAR image using two edge extraction methods respectively; An edge similarity calculation module, which is used to calculate the primary edge extraction similarity of the two edge extraction results and calculate the secondary edge extraction similarity; A secondary blurring processing module, which is used to compare with a set threshold t. If it is > t, then perform secondary blurring on the SAR image; A condition judgment and evaluation module, which is used to calculate the comprehensive quality evaluation value of the SAR image according to preset conditions.

[0090] The SAR image quality evaluation system of this embodiment is used to implement a SAR image quality evaluation method for the clarity of edge structures. The steps include: S1. Set a brightness threshold to separate the high-brightness spots in the SAR image, and calculate the aggregation density of the high-brightness spots in the SAR image ; S2. Use two edge extraction methods to perform edge extraction on the SAR image respectively, and calculate the primary edge extraction similarity of the two edge extraction results ; S3. Compare with the set threshold t . If > t , then perform secondary blurring on the SAR image, and perform the same processing as in step S2 on the secondary blurred image, and calculate the secondary edge extraction similarity ; S4. Calculate the comprehensive quality evaluation value of the SAR image according to the following conditions: When ≤ t , or > t and < , the expression of the comprehensive image quality evaluation value is: ; When > t , and ≥ , the expression of the comprehensive image quality evaluation value is:

[0091] In the formula, , is a preset weighting coefficient.

[0092] The present application also provides an electronic device for implementing various embodiments of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0093] Those skilled in the art can understand that the structure of the electronic device involved in the embodiments of the present application does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0094] Figure 9 Schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present application.

[0095] The electronic device includes, but is not limited to, components such as a processor and a memory. Those skilled in the art can understand that the structure of the electronic device involved in the embodiments of the present application does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0096] In the embodiments of the present application, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0097] In the embodiments of the present application, the processor may be implemented by using at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to execute the functions described herein. In some cases, such an implementation may be implemented in the controller. For a software implementation, an implementation of a process or function may be implemented with a separate software module that allows execution of at least one function or operation. The software code may be implemented by a software application (or program) written in any suitable programming language. The software code may be stored in the memory and executed by the controller.

[0098] In addition, the electronic device includes some functional modules not shown herein and will not be elaborated further.

[0099] Those skilled in the art to which the present application pertains can understand that various aspects of the electronic device provided by the present application can be implemented as a system, a method, or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to herein as "circuitry", "module", or "system".

[0100] The present application also provides a storage medium in which a program product capable of implementing the SAR image quality evaluation method for edge structure clarity is stored. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0101] The storage medium can adopt any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0102] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A SAR image quality evaluation method for edge structure clarity, characterized in that Including: S1. Set a brightness threshold, isolate the bright spots in the SAR image, and calculate the aggregation density of the bright spots in the SAR image ; S2. Edge extraction is performed on the SAR image using two edge extraction methods respectively, and the initial edge extraction similarity of the two edge extraction results is calculated ; S3. Compare with the set threshold t . If > t , perform secondary blurring on the SAR image and perform the same processing as in step S2 on the secondary blurred image to calculate the secondary edge extraction similarity ; S4. Calculate the comprehensive quality evaluation value of the SAR image according to the following conditions: When ≤ t or > t and < the expression for the comprehensive evaluation value of the image quality is: ； When > t and ≥ the expression for the comprehensive evaluation value of the image quality is: In the formula, , is a preset weighting coefficient.

2. The SAR image quality evaluation method according to claim 1, wherein Step S1 specifically includes: S101. Normalize the grayscale of the SAR image and set the brightness threshold , and perform binarization on the SAR image with normalized grayscale. The formula is as follows: wherein, is the pixel value after binarization separation of point ; is a point the normalized pixel value; S102. Set the scale to and traverse the binarized SAR image with a window of this size, recording the number of pixels with a pixel value of 1 within each pixel window , calculate the bright spot density within the window . The bright spot density within the k-th window is expressed as: ； S103. Count all non-zeros in number , and calculate as follows: 。 3. The SAR image quality evaluation method according to claim 1, wherein In step S2, the LoG operator and the Canny operator are respectively used for edge extraction.

4. The SAR image quality evaluation method according to claim 3, characterized in that, In step S2, The calculation formula is: In the formula, , respectively represent the edge extraction images obtained by using two edge extraction methods; Indicates in the figure midpoint pixel value; Indicates in the figure midpoint of the pixel value; p is the number of columns of the edge-extracted image, q is the number of rows of the edge-extracted image.

5. The SAR image quality evaluation method according to claim 1, wherein, In step S3, the secondary blur adopts Gaussian blur or defocus blur.

6. The SAR image quality evaluation method according to claim 5, characterized in that, The transfer function of defocus blur is: In the formula, represents the first-kind Bessel function of the first order; R is the defocus radius; and are frequency domain coordinates, u , v respectively representing the frequency components in the horizontal and vertical directions.

7. The SAR image quality evaluation method according to claim 5, wherein The transfer function of Gaussian blur is expressed as: In the formula, is the standard deviation of the Gaussian kernel.

8. A SAR image quality evaluation system for edge structure clarity, characterized in that, To implement the SAR image quality evaluation method according to any one of claims 1-7, including: An SAR image input module for inputting an SAR image; An image preprocessing module for setting a brightness threshold and separating the highlight spots in the SAR image; A highlight spot density calculation module for calculating the aggregation density of the highlight spots in the SAR image; An edge extraction module for respectively performing edge extraction on the SAR image using two edge extraction methods; An edge similarity calculation module for calculating the primary edge extraction similarity of the two edge extraction results and calculating the secondary edge extraction similarity; The secondary blurring processing module is used to compare with the set threshold value t and if > t , then perform secondary blurring on the SAR image; A condition judgment and evaluation module for calculating the comprehensive quality evaluation value of the SAR image according to preset conditions.

9. An electronic device, characterized in that, Including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor is used to implement the steps of the SAR image quality evaluation method according to any one of claims 1-7 when executing the computer program.

10. A storage medium, characterized in that, A computer program is stored on a storage medium, and when the computer program is executed by a processor, the steps of the SAR image quality evaluation method according to any one of claims 1-7 are implemented.

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