A microscale natural speckle quality assessment method and system based on image information parameters

Abstract

This invention provides a method and system for evaluating the quality of microscale natural speckle patterns based on image information parameters. The method includes the following steps: Step 1, acquiring and processing natural speckle images of a specimen; Step 2, calculating quantitative evaluation parameters of the speckle images; Step 3, obtaining the Shannon entropy of the directional gradient of the speckle images based on the quantitative evaluation parameters obtained in Step 2; Step 4, evaluating the microscale natural speckle quality information based on the Shannon entropy of the directional gradient of the speckle images. This method and system achieve a comprehensive evaluation of speckle quality, which is beneficial for equipment or systems to screen out high-quality natural speckle images during the use of digital images, reducing system errors and improving the accuracy of digital image-related systems in measuring displacement and strain.

Description

Technical Field

[0001] This invention relates to the fields of image processing and numerical computation, specifically to a microscale natural speckle quality evaluation method and system based on image information parameters. Background Technology

[0002] Digital Image Correlation (DIC) is a non-invasive full-field stress-strain measurement technique that stands out for its relaxed experimental environment requirements, strong anti-interference capabilities, and excellent measurement accuracy.

[0003] However, artificial speckle patterns face two core challenges in DIC measurements: fabricating suitable artificial speckles for DIC experiments at the microscale is quite challenging, and artificial speckles suffer from insufficient adhesion and asynchronous deformation during microscale measurements. Natural speckle patterns, with their superior mechanical properties, successfully overcome the difficulty of artificial speckles deforming synchronously with the sample surface after attachment, demonstrating significant potential to replace artificial speckles for measurements at the microscale.

[0004] Nevertheless, there is currently a lack of systematic research on the quality information of microscale natural speckle and its evaluation criteria. Therefore, it is urgent to propose a parameter system for evaluating the quality of natural speckle in digital image correlation calculations, and to evaluate the quality of natural speckle based on this parameter system. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a microscale natural speckle quality evaluation method and system based on image information parameters. This method combines the parameter of anisotropic first-order gray-level gradient and Shannon entropy—directional gradient Shannon entropy (DGSE)—to achieve a comprehensive evaluation of speckle quality. This facilitates the selection of high-quality natural speckle images by equipment or systems in digital image-based applications, reduces system errors, and improves the accuracy of displacement and strain measurements in digital image-related systems.

[0006] The technical solution of the present invention is as follows:

[0007] A method for evaluating the quality of microscale natural speckle based on image information parameters includes the following steps:

[0008] Step 1: Acquire and process natural speckle images of the specimen;

[0009] Step 2: Calculate the quantitative parameters of the speckle image;

[0010] Step 3: Obtain the directional gradient Shannon entropy of the speckle image based on the quantitative parameters of the speckle image;

[0011] Step 4: Evaluate the microscale natural speckle quality information based on the Shannon entropy of the speckle image's directional gradient.

[0012] Further, step 1, acquiring and processing natural speckle images of the specimen, includes the following steps:

[0013] Step 11: Collect images of the parts of the specimen that need to be measured by digital image correlation (DIC), and then obtain the height H and width W of the images to facilitate the subsequent calculation of the directional gradient Shannon entropy;

[0014] Step 12: Perform grayscale processing on the image to obtain a speckle image, wherein the speckle in the speckle image is the microscopic surface texture of the sample.

[0015] Further, step 2, calculating the quantitative evaluation parameters of the speckle image, includes the following steps:

[0016] Step 21: The quantitative evaluation parameter is the gray-level gradient of the speckle image. Feature extraction is performed on the speckle image to obtain a gray-level matrix. Python is used to extract the gray level of the speckle pattern that has been grayscaled and convert it into a gray-level matrix. Each element of the gray-level matrix represents the gray-level value of the corresponding pixel.

[0017] Step 22: Calculate the gray-level matrix using the Sobel operator to obtain the gray-level gradient matrix;

[0018] Step 23: Calculate the first-order gray-level gradient g of the subset direction X and direction Y from the gray-level gradient matrix obtained in the previous step. ijx and g ijy ;

[0019] Step 24: Calculate the gray-level gradient of the subset, g. ij The calculation formula is as follows:

[0020]

[0021] Where g ijx and g ijy Let X be the first-order gray-level gradient of the subset in the direction X and Y; i = 1, 2, 3…M x M x This represents the total number of rows of pixels in the speckle image, j = 1, 2, 3…M Y M Y This represents the total number of columns of pixels in a speckle image.

[0022] Furthermore, in step 23, the first-order gray-level gradient g of the subset in direction X and direction Y is calculated and obtained. ijx and g ijy It includes the following steps:

[0023] Step 231: Define the Sobel operator. The Sobel operator contains two kernels: one for detecting the gradient in the X direction of horizontal edges and the other for detecting the gradient in the Y direction of vertical edges.

[0024] Step 232: Image convolution. Perform a convolution operation between the Sobel operator and the image subset, and calculate the gradient values ​​in the X and Y directions respectively. For the gradient in the X direction, convolve the image subset with the kernel of the Sobel operator in the X direction to obtain the gradient value in the X direction. For the gradient in the Y direction, convolve the image subset with the kernel of the Sobel operator in the Y direction to obtain the gradient value in the Y direction.

[0025] Step 233: Gradient calculation. Calculate the gradient value of each pixel in the X and Y directions respectively. These gradient values ​​will represent the first-order gray-level gradient of each pixel in the image.

[0026] Step 234: Gradient magnitude and direction. Based on the calculated gradient values ​​in the X and Y directions, calculate the gradient magnitude and direction for each pixel.

[0027] Further, step 3, obtaining the directional gradient Shannon entropy of the speckle image based on the quantitative evaluation parameters of the speckle image, includes:

[0028] The expression for the directional gradient Shannon entropy is:

[0029]

[0030] Among them, DGSE x DGSE is the directional gradient Shannon entropy in the X direction. y The directional gradient of Shannon entropy in the Y direction. g ijx and g ijy These are the first-order gray-level gradients of the subset in the X and Y directions, respectively. The sum of the grayscale gradients of this subset is given by W, where W is the total width of the speckle image and H is the total height of the speckle image.

[0031] Furthermore, step 4, evaluating the microscale speckle quality information based on the directional gradient Shannon entropy of the speckle image, includes the following steps:

[0032] Step 41: Acquire multiple speckle images, and extract the Shannon entropy of the directional gradient corresponding to the multiple speckle images according to the methods in Steps 1 to 3;

[0033] Step 42: Compare and analyze the Shannon entropy of the directional gradient corresponding to the multiple speckle images. The obtained numerical results are consistent with the variation law of the average offset error of the sub-pixel displacement measured by DIC. That is, the smaller the average sub-pixel offset error of the image, the smaller the Shannon entropy of the image's directional gradient; the larger the average sub-pixel offset error of the image, the larger the Shannon entropy of the image's directional gradient.

[0034] Step 43: From this, we can conclude that the smaller the Shannon entropy value of the directional gradient of the speckle image, the better the quality of the speckle.

[0035] This invention also provides a microscale natural speckle quality assessment system based on image information parameters, comprising:

[0036] The module for acquiring natural speckle images acquires and processes natural speckle images of the specimen.

[0037] The module for calculating quantitative evaluation parameters is used to calculate the quantitative evaluation parameters of the speckle image.

[0038] The module for obtaining the directional gradient Shannon entropy of the speckle image obtains the directional gradient Shannon entropy of the speckle image based on the quantitative evaluation parameters of the speckle image.

[0039] The speckle quality evaluation module evaluates the speckle quality of image characteristic parameters based on the directional gradient Shannon entropy parameter of the speckle image.

[0040] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it performs the steps of the above-described method for evaluating the quality of microscale natural speckle based on image information parameters.

[0041] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating the quality of microscale natural speckle based on image information parameters.

[0042] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating the quality of microscale natural speckle based on image information parameters.

[0043] The present invention has the following beneficial technical effects:

[0044] 1. The present invention provides a microscale natural speckle quality evaluation method and system based on image information parameters, which for the first time combines the parameter of anisotropic first-order gray-level gradient and Shannon entropy—directional gradient Shannon entropy (DGSE)—to achieve a comprehensive evaluation of speckle quality.

[0045] 2. The microscale natural speckle quality evaluation method and system based on image information parameters described in this invention is beneficial for equipment or systems to screen out high-quality natural speckle images during the use of digital images, reduce system errors, and improve the accuracy of digital image-related systems in measuring displacement and strain.

[0046] 3. The microscale natural speckle quality evaluation method and system based on image information parameters described in this invention have been verified through specific examples, and it has been found that the evaluation results are accurate and have broad application prospects. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the grayscale gradient parameters of the present invention;

[0048] Figure 2 These are the speckle feature diagram and grayscale distribution diagram of the present invention;

[0049] Figure 3 a is the speckle pattern of the present invention. Figure 2 a- Figure 2 g) Average offset error in the X direction;

[0050] Figure 3 b is the speckle pattern of the present invention. Figure 2 a- Figure 2 g) Average offset error in the Y direction;

[0051] Figure 4 The average offset error of the speckle pattern in the X direction at a sub-pixel displacement of 0.25 is the average offset error of the speckle pattern in the present invention. Figure 2 a- Figure 2 g). Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the following description will be provided in conjunction with the appendix. Figure 1-4 The present invention will be described in further detail below.

[0053] This invention provides a method for evaluating the quality of microscale natural speckle based on image information parameters, comprising the following steps:

[0054] Step 1: Acquire and process natural speckle images of the specimen, specifically including:

[0055] Step 11: Collect images of the parts of the specimen that need to be measured by digital image correlation (DIC), and then obtain the height H and width W of the images to facilitate the subsequent calculation of the directional gradient Shannon entropy;

[0056] Step 12: Perform grayscale processing on the image to obtain a speckle image, wherein the speckle in the speckle image is the microscopic surface texture of the sample.

[0057] Step 2: Calculate the quantitative evaluation parameters of the speckle image, specifically including:

[0058] Step 21: The quantitative evaluation parameter is the gray-level gradient of the speckle image. Feature extraction is performed on the speckle image to obtain a gray-level matrix. Python is used to extract the gray level of the speckle pattern that has been grayscaled and convert it into a gray-level matrix. Each element of the gray-level matrix represents the gray-level value of the corresponding pixel.

[0059] Step 22: Calculate the gray-level matrix using the Sobel operator to obtain the gray-level gradient matrix;

[0060] Step 23: Calculate and obtain the first-order gray-level gradient g of the subset in direction X and direction Y. ijx and g ijy It includes the following steps:

[0061] Step 231, Define the Sobel operator: The Sobel operator contains two kernels, one for detecting the gradient in the X direction of horizontal edges and the other for detecting the gradient in the Y direction of vertical edges;

[0062] Step 232, Image Convolution: Perform a convolution operation between the Sobel operator and the image subset, and calculate the gradient values ​​in the X and Y directions respectively. For the gradient in the X direction, convolve the image subset with the kernel of the Sobel operator in the X direction to obtain the gradient value in the X direction. For the gradient in the Y direction, convolve the image subset with the kernel of the Sobel operator in the Y direction to obtain the gradient value in the Y direction.

[0063] Step 233: Gradient Calculation: Calculate the gradient value of each pixel in the X and Y directions respectively. These gradient values ​​will represent the first-order gray-level gradient of each pixel in the image.

[0064] Step 234, Gradient Magnitude and Direction: Based on the calculated gradient values ​​in the X and Y directions, calculate the gradient magnitude and direction for each pixel.

[0065] Step 24: Calculate the gray-level gradient of the subset, g. ij The calculation formula is as follows:

[0066]

[0067] Where g ijx and g ijy Let X be the first-order gray-level gradient of the subset in the direction X and Y; i = 1, 2, 3…M x M x This represents the total number of rows of pixels in the speckle image, j = 1, 2, 3…M Y M YThis represents the total number of columns of pixels in the speckle image;

[0068] Step 3: Obtain the directional gradient Shannon entropy of the speckle image based on the quantitative evaluation parameters of the speckle image, specifically including:

[0069] The expression for the directional gradient Shannon entropy is:

[0070]

[0071] Among them, DGSE x DGSE is the directional gradient Shannon entropy in the X direction. y The directional gradient of Shannon entropy in the Y direction. g ijx and g ijy These are the first-order gray-level gradients of the subset in the X and Y directions, respectively. The sum of grayscale gradients of this subset, W is the total width of the speckle image, and H is the total height of the speckle image.

[0072] Step 4: Evaluate the microscale natural speckle quality information based on the directional gradient Shannon entropy of the speckle image, specifically including:

[0073] Step 41: Acquire multiple speckle images, and extract the Shannon entropy of the directional gradient corresponding to the multiple speckle images according to the methods in Steps 1 to 3;

[0074] Step 42: Compare and analyze the Shannon entropy of the directional gradient corresponding to the multiple speckle images. The numerical results obtained are consistent with the variation law of the average offset error of the subpixel displacement measured by DIC. That is, the smaller the average offset error of the subpixel image, the smaller the Shannon entropy of the directional gradient of the image; the larger the average offset error of the subpixel image, the larger the Shannon entropy of the directional gradient of the image.

[0075] Step 43: From this, we can conclude that the smaller the Shannon entropy value of the directional gradient of the speckle image, the better the quality of the speckle.

[0076] To more clearly and intuitively illustrate the technical solution of this invention, the following Example 1 is used for description. Example 1 involves seven speckle images obtained using different techniques, such as... Figure 2 As shown, Figure 2 (a) shows speckle patterns caused by the adhesion of micron-sized particles. Figure 2 (b) is a computer-generated Gaussian speckle pattern with a density of 50% and an offset of 50%. Figure 2 (c) is the second-phase speckle pattern of additively manufactured AZ31 after etching. Figure 2 (d) shows the traditional grain speckle pattern of 316L stainless steel after etching. Figure 2(e)-2(g) are natural speckles (a combination of dendritic and mesh speckles) in etched additively manufactured 316L stainless steel.

[0077] These seven speckle patterns differ slightly in grayscale distribution and morphological characteristics. Among them, computer-generated Gaussian speckle (Gaussian speckle) Figure 2 b) Uniform grayscale distribution, artificial speckle pattern ( Figure 2 a) and second-phase speckle ( Figure 2 c) has a better grayscale distribution than other natural speckle patterns. Figure 2 (e)- Figure 2 (g)).

[0078] Figure 2 (a)- Figure 2 (g) The speckle pattern has a grayscale distribution map in the lower right corner (see the blue curve), showing the best speckle distribution. Figure 2 (b) For example, its gray level distribution is average. If the gray level distribution map has a peak, it indicates that most of the gray level is distributed in that area, that is, the gray level distribution is not uniform.

[0079] Measure the height H and width W of each speckle image;

[0080] Feature extraction is performed on the speckle image to obtain the gray-level matrix;

[0081] The gray-level matrix is ​​calculated to obtain the gray-level gradient matrix;

[0082] Figure 3 a is the invention Figure 2 a- Figure 2 The average offset error of the speckle pattern in the X direction in g, from Figure 3 As can be seen from a, the computer-generated Gaussian speckle ( Figure 2 b) The average offset error distribution in the X direction is uniform, and the error is minimal.

[0083] Figure 3 b is the present invention Figure 2 a- Figure 2 The average offset error of the speckle pattern in the Y direction in g, from Figure 3 b shows that the computer-generated Gaussian speckle ( Figure 2 b) The average offset error distribution in the Y direction is uniform, and the error is minimal.

[0084] Generally speaking, speckled patterns are divided into three categories: artificial speckled patterns (…). Figure 2 a, Figure 2 b) Homogeneous natural speckles ( Figure 2 c. Figure 2 d、 Figure 2 g), heterogeneous natural speckle ( Figure 2 e Figure 2 f);

[0085] This embodiment 1 uses Figure 2 (a)- Figure 2 Taking the speckle image (g) as an example, calculate their first gray-level gradient g. ijx and g ijy Seven speckle images were obtained. ijx and g ijy After calculating the value, follow the formula:

[0086]

[0087] The corresponding g is calculated. ij ;

[0088] Substitute all the obtained values ​​into the following formula:

[0089]

[0090] The following table is obtained;

[0091] Table 1. Orientation gradients and Shannon entropies for seven types of speckle images.

[0092]

[0093] By comparing the directional gradient Shannon entropy of various speckle images in Table 1 above, it can be seen that... Figure 2 (b) has the best speckle image quality.

[0094] To verify the effectiveness of this method in evaluating speckle images, this invention provides another embodiment 2. Embodiment 2 uses numerical simulation and the phase shift theorem in Fourier transform to perform precise and controllable spatial translation of each speckle pattern. It proposes a method to evaluate the quality of a speckle image by calculating the average offset error of the speckle pattern. The specific steps are as follows:

[0095] (1) Select speckle images: Select one or more speckle images as the analysis objects. These images should have clear speckle patterns to facilitate subsequent processing.

[0096] (2) Apply Fourier transform: Perform Fourier transform on the selected speckle image to convert the image from the spatial domain to the frequency domain.

[0097] (3) Using the phase shift theorem for translation: The phase shift theorem of Fourier transform shows that translation in the spatial domain corresponds to phase change in the frequency domain.

[0098] (4) Apply phase change: Apply the above phase change in the frequency domain to achieve precise translation of the image.

[0099] (5) Applying the inverse Fourier transform: The frequency domain image after phase adjustment is converted back to the spatial domain through the inverse Fourier transform to obtain the translated image.

[0100] (6) Compare the images before and after translation: Use image processing algorithms (such as cross correlation) to compare the images before and after translation and calculate the error between the actual translation and the expected translation.

[0101] (7) Calculate error statistics: Calculate the average offset error of a series of translation experiments and analyze the statistical characteristics of the offset error, such as the mean and standard deviation.

[0102] (8) Error analysis: The quality of the speckle image is evaluated based on the magnitude and consistency of the average offset error. Smaller and more consistent offset errors indicate higher image quality, making it suitable for accurate image correlation analysis.

[0103] from Figure 3 It can be seen that the speckle pattern of the present invention ( Figure 2 a- Figure 2 g) The average offset error is greatest in the X and Y directions when the subpixel displacement is 0.25.

[0104] Figure 4 Each speckle image was calculated ( Figure 2 a- Figure 2 g) The average offset error in the X direction at a sub-pixel displacement of 0.25, from Figure 4 It can be seen Figure 2 (b) has the smallest average offset error in its speckle image, therefore, Figure 2 (b) has the best speckle image quality.

[0105] By comparing and analyzing the Shannon entropy of the directional gradient and the average offset error of the subpixel displacement, it can be seen that the two are basically consistent. That is, the smaller the average offset error of the subpixel displacement of the speckle image, the smaller the Shannon entropy of the directional gradient of the speckle image; the larger the average offset error of the subpixel displacement of the speckle image, the larger the Shannon entropy of the directional gradient of the speckle image.

[0106] The above verification results show that the microscale natural speckle quality evaluation method based on image information of the present invention can effectively evaluate the quality of natural speckle, and its evaluation results are accurate and have broad application prospects.

[0107] In addition, the present invention also provides a speckle quality evaluation system based on image characteristic parameters, comprising:

[0108] The module for acquiring natural speckle images acquires and processes natural speckle images of the specimen.

[0109] The module for calculating quantitative evaluation parameters is used to calculate the quantitative evaluation parameters of the speckle image.

[0110] The module for obtaining the directional gradient Shannon entropy of the speckle image obtains the directional gradient Shannon entropy of the speckle image based on the qualitative evaluation parameters of the speckle image.

[0111] The speckle quality evaluation module evaluates the speckle quality of image characteristic parameters based on the directional gradient Shannon entropy parameter of the speckle image.

[0112] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it performs the steps of the above-described method for evaluating the quality of microscale natural speckle based on image information parameters.

[0113] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating the quality of microscale natural speckle based on image information parameters.

[0114] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for evaluating the quality of microscale natural speckle based on image information parameters.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit this application. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the quality of microscale natural speckle based on image information parameters, characterized in that, Includes the following steps: Step 1: Acquire and process natural speckle images of the specimen; Step 2: Calculate the quantitative evaluation parameters of the speckle image, where the quantitative evaluation parameters are the gray-level gradient of the speckle image; Step 3: Obtain the directional gradient Shannon entropy of the speckle image based on the quantitative evaluation parameters of the speckle image, where the expression for the directional gradient Shannon entropy is: in, The directional gradient of Shannon entropy in the X direction. The directional gradient of Shannon entropy in the Y direction. , and Subsets direction and The first-order gradient of grayscale in the direction, , is the sum of the gray-level gradients of the subset, W is the total width of the speckle image, and H is the total height of the speckle image; Step 4: Evaluate the microscale natural speckle quality information based on the directional gradient Shannon entropy of the speckle image. The smaller the directional gradient Shannon entropy value of the speckle image, the better the speckle quality.

2. The method for evaluating the quality of microscale natural speckle based on image information parameters according to claim 1, characterized in that, Step 1: Acquire and process natural speckle images of the specimen, including the following steps: Step 11: Acquire images of the portions of the specimen that require digital image correlation measurement, and then obtain the height of the images. and width This facilitates subsequent calculation of the directional gradient Shannon entropy; Step 12: Perform grayscale processing on the image to obtain a speckle image, wherein the speckle in the speckle image is the microscopic surface texture of the sample.

3. The method for evaluating the quality of microscale natural speckle based on image information parameters according to claim 1, characterized in that, Step 2: Calculate the quantitative evaluation parameters of the speckle image, including the following steps: Step 21: The quantitative evaluation parameter is the gray-level gradient of the speckle image. Feature extraction is performed on the speckle image to obtain the gray-level matrix. Step 22: Calculate the gray-level matrix using the Sobel operator to obtain the gray-level gradient matrix; Step 23: Calculate the first-order gray-level gradients of the subset directions X and Y from the gray-level gradient matrix obtained in the previous step. and ; Step 24: Calculate the gray-level gradient of the subset. The calculation formula is as follows: in and The direction of the subset in that direction and direction The first-order grayscale gradient; =1,2,3… , This represents the total number of rows of pixels in the speckle image. =1,2,3… , This represents the total number of columns of pixels in a speckle image.

4. The method for evaluating the quality of microscale natural speckle based on image information parameters according to claim 3, characterized in that, Step 23: Calculate and obtain the subset direction and direction First-order gray gradient and It includes the following steps: Step 231, Define the Sobel operator: The Sobel operator contains two kernels, one for detecting the gradient in the X direction of horizontal edges and the other for detecting the gradient in the Y direction of vertical edges; Step 232, Image Convolution: Perform a convolution operation between the Sobel operator and the image subset, and calculate the gradient values ​​in the X and Y directions respectively. For the gradient in the X direction, convolve the image subset with the kernel of the Sobel operator in the X direction to obtain the gradient value in the X direction. For the gradient in the Y direction, convolve the image subset with the kernel of the Sobel operator in the Y direction to obtain the gradient value in the Y direction. Step 233: Gradient Calculation: Calculate the gradient value of each pixel in the X and Y directions respectively. These gradient values ​​will represent the first-order gray-level gradient of each pixel in the image. Step 234, Gradient Magnitude and Direction: Based on the calculated gradient values ​​in the X and Y directions, calculate the gradient magnitude and direction for each pixel.

5. The method for evaluating the quality of microscale natural speckle based on image information parameters according to claim 1, characterized in that, Step 4: Evaluate the microscale speckle quality information based on the directional gradient Shannon entropy of the speckle image, including the following steps: Step 41: Acquire multiple speckle images, and extract the Shannon entropy of the directional gradient corresponding to the multiple speckle images according to the methods in Steps 1 to 3; Step 42: Compare and analyze the Shannon entropy of the directional gradient corresponding to the multiple speckle images. The numerical results obtained are consistent with the variation law of the average deviation error of the subpixel displacement measured by the digital image correlation method. That is, the smaller the average deviation error of the subpixel image, the smaller the Shannon entropy of the directional gradient of the image; the larger the average deviation error of the subpixel image, the larger the Shannon entropy of the directional gradient of the image. Step 43: From this, we can conclude that the smaller the Shannon entropy value of the directional gradient of the speckle image, the better the quality of the speckle.

6. A microscale natural speckle quality evaluation system based on image information parameters, characterized in that, include: The module for acquiring natural speckle images acquires and processes natural speckle images of the specimen. The module for calculating quantitative evaluation parameters is used to calculate the quantitative evaluation parameters of the speckle image, wherein the quantitative evaluation parameters are the gray-level gradient of the speckle image. The module for obtaining the directional gradient Shannon entropy of the speckle image obtains the directional gradient Shannon entropy of the speckle image based on the qualitative evaluation parameters of the speckle image. The expression for the directional gradient Shannon entropy is: in, The directional gradient of Shannon entropy in the X direction. The directional gradient of Shannon entropy in the Y direction. , and Subsets direction and The first-order gradient of grayscale in the direction, , where is the sum of the gray-level gradients of the subset, W is the total width of the speckle image, and H is the total height of the speckle image; The speckle quality evaluation module evaluates the speckle quality of image characteristic parameters based on the Shannon entropy parameter of the speckle image's directional gradient. The smaller the Shannon entropy value of the speckle image's directional gradient, the better the speckle quality.

7. An electronic device, characterized in that: The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor runs the computer program, it performs the steps of the method according to any one of claims 1-5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-5.

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