Three-dimensional digital image method based on optimal image subarea size adaptive calculation

Abstract

The invention discloses a three-dimensional digital image method (3D-DIC) based on adaptive calculation of an optimal image sub-region size, and the method comprises the steps: estimating the size of an initial image sub-region and the value range of the size of the image sub-region based on a speckle evaluation method of an SSSIG (Sum of Quality of Square) threshold value of the image sub-region; performing attempt DIC calculation of time sequence matching and stereo matching based on the size of the initial image subarea to obtain an initial displacement field, and estimating a high-order displacement derivative based on a local least square fitting method; calculating an optimal image sub-region size based on a high-order displacement derivative and speckle related parameters; the initial displacement field serves as an initial value, and a final image displacement result is obtained through optimization calculation by combining the optimal image sub-area size optimization of time sequence matching and stereo matching; based on simulation image data generated by a simulation stereoscopic vision system, it is verified that the self-adaptive algorithm used in the method has higher measurement precision than a conventional three-dimensional digital image correlation method.

Description

Technical Field

[0001] The present invention relates to the field of experimental solid mechanics and computer vision measurement technology, in particular to the field of three-dimensional digital image correlation method technology for measuring three-dimensional morphology, displacement and deformation of object surfaces, and in particular to a three-dimensional digital image correlation method based on adaptive calculation of optimal image sub-area size. Background Art

[0002] Three-dimensional digital image correlation (3D-DIC) is a non-contact optical measurement method that can measure the full-field morphology, displacement and deformation of materials and structures (including biological tissues). It has been widely used in solid mechanics, materials science, aerospace, civil engineering and other fields. This method can obtain the full-field three-dimensional morphology, displacement and strain of the surface of the test object by comparing the digital images collected by the stereo imaging system before and after the deformation of the object under test, and combining the computer stereo vision measurement principle. In practice, in order to accurately reconstruct and track the three-dimensional coordinates of each calculation point, stereo matching between different camera images and time matching between the same camera images are required. Whether it is stereo matching or time matching, it is necessary to perform nonlinear optimization of the correlation function and select appropriate matching parameters, mainly including the image sub-area size and shape function. However, existing 3D-DIC software often leaves parameter selection to the user to set manually, lacks clear guidance, and makes it difficult to obtain the optimal calculation results. Research shows that the selection of these matching parameters needs to fully consider the speckle quality during the experiment and the local deformation of the specimen. Therefore, the optimal calculation parameters required for the stereo matching and timing matching steps of each calculation point in the actual 3D-DIC calculation are not consistent, and the optimal calculation parameters need to be set separately for each calculation point. Therefore, how to achieve the adaptive selection of the optimal calculation parameters has become a key issue and important challenge in achieving adaptive and accurate 3D-DIC measurement.

[0003] Existing methods for adaptive selection of matching parameters in digital image correlation methods mainly include methods based on speckle quality assessment. This method only considers the speckle quality and proposes to use the sum of squared grayscale gradients (SSSIG) of image sub-area as an evaluation indicator by evaluating the random error of DIC image matching. Specifically, this method first sets an SSSIG threshold and then gradually increases the image sub-area size until the SSSIG threshold is reached. In this way, the optimal image sub-area size is selected for each calculation point, thereby reducing the random error of matching of the full-field calculation points.

[0004] However, selecting the optimal image sub-area size requires considering both the local speckle quality and the local deformation size. The speckle quality assessment method only considers the local speckle quality, that is, it only optimizes the random error in the matching. Therefore, this method may fail to measure non-uniform complex deformations and cannot select the optimal parameters for complex deformations. This means that in practical applications, relying solely on the speckle quality assessment method may not achieve the required matching accuracy and stability.

[0005] Existing methods may fail in the face of complex deformations and cannot consider both local speckle quality and local deformation at the same time. These problems limit the practicality and reliability of parameter adaptive selection methods in practical applications. Summary of the invention

[0006] The purpose of the present invention is to address several defects existing in the prior art, and propose an accurate three-dimensional digital image correlation method based on adaptive calculation of the optimal image sub-area size, which can automatically select an adaptive accurate three-dimensional digital image correlation algorithm with two optimal matching parameters. The inventive concept is that in 3D-DIC, in order to achieve three-dimensional reconstruction of the calculation points and three-dimensional displacement tracking, stereo matching between different cameras and time matching of the same camera are required. Due to the difference in viewing angles between different cameras, the local deformation of stereo matching is more complicated, so a more complicated second-order shape function needs to be used; while the time matching between the same camera only needs to match the deformation of the specimen at the same viewing angle, and the local deformation of the time matching is usually simpler, so a first-order shape function is generally used. Based on this, the present invention uses fixed first-order shape functions and second-order shape functions in time matching and stereo matching, respectively, to better meet the needs of different matching scenarios.

[0007] The present invention provides a three-dimensional digital image method based on adaptive calculation of optimal image sub-area size, comprising:

[0008] S1, obtaining a reference image of the target area, estimating relevant parameters in an image parameter formula applied to the reference image, and estimating an initial image sub-area size and a value range of the image sub-area size based on a speckle evaluation method using an SSSIG threshold;

[0009] S2, performing temporal matching and stereo matching DIC calculation based on the initial image sub-area size, thereby obtaining an initial displacement field, and estimating high-order displacement derivatives based on a local least squares fitting method;

[0010] S3, calculating the optimal image sub-region size based on the high-order displacement derivative;

[0011] S4, taking the initial displacement field in S2 as an initial value, combining with the optimal image sub-area size obtained in S3, optimizing the displacement calculation results of temporal matching and stereo matching based on an adaptive method and a conventional method to obtain corresponding image displacements, and obtaining a final image displacement result;

[0012] S5, performing three-dimensional reconstruction based on the corresponding image displacement and combining system parameters of the simulation system, so as to obtain three-dimensional displacement results of all calculation points.

[0013] Preferably, the S1 comprises:

[0014] S11, obtaining a reference image of the target area;

[0015] S12, estimating image noise using a Laplacian operator on the reference image;

[0016] S13, and using the Barron operator to calculate the MIG value of the reference image sub-area of ​​the calculation point;

[0017] S14, using a speckle evaluation method based on an SSSIG threshold to estimate an initial image sub-region size and a value range of the image sub-region size; wherein the initial image sub-region size and the value range of the image sub-region size are determined by an initial value of the image sub-region size, an upper bound of the image sub-region size, and a lower bound of the image sub-region size; the speckle evaluation method based on an SSSIG threshold is directly used for estimating the initial value of the image sub-region size and the upper bound of the image sub-region size; and the lower bound of the image sub-region size is set to a fixed value of pixels by default;

[0018] Preferably, S2 includes:

[0019] S21, performing temporal matching and stereo matching DIC calculation on the calculation points based on the size of the initial image sub-area, so as to obtain an initial displacement field, wherein the temporal matching uses a first-order shape function and the stereo matching uses a second-order shape function;

[0020] S22, fitting the local displacement fields near the calculation points respectively based on the local least squares fitting method; wherein the local least squares fitting method is implemented based on a second-order polynomial or a fourth-order polynomial, wherein the timing matching uses the second-order polynomial, and the stereo matching uses the fourth-order polynomial.

[0021] S23, estimate higher-order displacement derivatives based on the fitted polynomial coefficients.

[0022] Preferably, the S22 may estimate the high-order displacement derivatives by replacing the local least squares fitting method with a numerical differentiation method.

[0023] Preferably, S3 includes:

[0024] S31, according to the estimated parameters, the optimal image sub-region sizes for temporal matching and stereo matching are respectively calculated in two formulas; and the optimal image sub-region sizes for temporal matching and stereo matching are set to the calculated image sub-region sizes;

[0025] S32: If the calculated image sub-region size exceeds the set image sub-region range, truncation processing is performed according to the image sub-region range.

[0026] Preferably, the S31 is used to obtain the optimal image sub-area size for 3D-DIC timing matching and stereo matching, including:

[0027] (1) The random error caused by image noise, which plays a major role in the DIC matching process, and the systematic error of undermatching are combined into the following mean absolute error (MAE):

[0028]

[0029] Among them, e d is the undermatched system error, σ d is the random error caused by image noise;

[0030] (2) Substitute the existing theoretical formula (2) for quantifying the random error of DIC and the existing theoretical formula (3) for quantifying the systematic error of DIC undermatching into formula (1) to calculate MAE d The calculation formula for the minimum value of the image sub-area size is used to obtain the optimal image sub-area size. Formulas (2) and (3) are the error estimation formulas for the horizontal displacement u, and the error formula for the vertical displacement v is similar. x and MIG y is the average grayscale gradient of the image in the horizontal and vertical directions, σ n is the standard deviation of image noise; where formula (2) and formula (3) are respectively:

[0031]

[0032] Specifically, the total error using first-order shape function matching is:

[0033]

[0034] in, is the Laplace operator; and Represent the second-order partial derivative operators in the x-direction and y-direction respectively; due to MAE d,1stThe function curve of the image sub-area size parameter M is U-shaped, so we only need to let Then solving this equation gives the optimal image subregion size M using the first-order shape function: 1st :

[0035]

[0036] in, The symbol means taking the integer part of x;

[0037] Similarly, the total error of matching using second-order shape functions is:

[0038]

[0039] Where R and S are expressed as:

[0040]

[0041] Similarly, let MAE d,2nd The partial derivative of the image subregion size parameter M is 0, that is: Solving this equation yields the optimal image subregion size M using a second-order shape function: 2nd :

[0042]

[0043] Preferably, the adaptive algorithm is an inverse combined Gauss-Newton algorithm.

[0044] Beneficial effects of the method of the present invention:

[0045] (1) The present invention fully considers the deficiencies of the prior art in the speckle quality evaluation method and proposes an optimal matching parameter selection algorithm combined with the speckle quality evaluation method. By comprehensively considering the speckle quality and local deformation, the algorithm can more accurately select the optimal calculation parameters, thereby improving the accuracy and precision of the matching.

[0046] (2) Based on the principle of minimizing the synthesis error, the present invention adopts the synthesis mean absolute error as the objective function, and then derives the explicit calculation formula of the optimal image sub-region size. The explicit calculation formula of the image sub-region size makes it unnecessary for the adaptive algorithm to perform iterative calculations, thereby greatly improving the calculation efficiency and reducing the calculation time and implementation complexity.

[0047] (3) In order to estimate the parameters in the explicit formula, the present invention directly uses the speckle quality evaluation method to obtain the initial calculation parameters, avoiding the process of iteratively calculating the parameters. This can ensure the accuracy of the initial displacement derivative estimation and further improve the reliability and practicality of the algorithm.

[0048] (4) The present invention takes into account the characteristics of temporal matching and stereo matching, and directly uses first-order shape functions and second-order shape functions to perform temporal matching and stereo matching respectively, which effectively improves the calculation efficiency of parameter selection and further simplifies the complexity of the algorithm. These characteristics make the present invention more applicable and reliable in practical applications, and can provide a more effective and reliable solution for the selection of matching parameters of three-dimensional digital image correlation methods.

[0049] (5) It has the characteristics of high precision, automation and simple implementation, including:

[0050] High-precision representation: The optimal calculation parameters selected by the adaptive method fully consider the local speckle quality and local deformation. Therefore, high-precision 3D-DIC calculation can be achieved based on the optimal calculation parameters selected by the adaptive method.

[0051] Automatic representation: The present invention adopts a speckle evaluation method based on SSSIG threshold to estimate the initial image sub-area size and the value range of the image sub-area size, thereby effectively ensuring the accuracy of the initial displacement estimation and automatically truncating unreasonable image sub-area sizes, thereby improving the stability and effectiveness of the algorithm.

[0052] Simple implementation: This method has a clear process and is easy to implement. It can be realized by simply modifying the existing 3D-DIC implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 The figure is a calculation flow chart of a 3D-DIC adaptive method provided according to an embodiment of the present invention.

[0055] Figure 2 A schematic diagram of a sinusoidal displacement field simulation data generation experiment provided according to an embodiment of the present invention.

[0056] Figure 3 is the optimal image sub-region size obtained by the adaptive method provided in the embodiment of the present invention. Figure 3 (a) is a schematic diagram of the optimal image sub-region size for stereo matching; Figure 3 (b) is a schematic diagram of the optimal image sub-area size for left camera timing matching; Figure 3 (c) is a schematic diagram of the optimal image sub-area size for timing matching of the right camera.

[0057] Figure 4 The displacement fields are calculated by the conventional 3D-DIC method and the adaptive method provided in the embodiments of the present invention.

[0058] Figure 5 It is the strain field calculated by the conventional 3D-DIC and adaptive methods provided in the embodiments of the present invention.

[0059] Figure 6 The horizontal displacement and strain mean distribution is provided according to an embodiment of the present invention. Figure 6 (a) is a schematic diagram of the mean displacement distribution in the horizontal direction; Figure 6 (b) is a schematic diagram of the mean strain distribution in the horizontal direction. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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.

[0061] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0062] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] Embodiment 1

[0064] This embodiment provides a three-dimensional digital image method based on adaptive calculation of optimal image sub-region size, including:

[0065] S1, obtaining a reference image of a target area, estimating relevant parameters in an image parameter formula applied to the reference image, and estimating an initial image sub-area size and a value range of the image sub-area size using a speckle evaluation method based on an SSSIG threshold;

[0066] As a preferred embodiment, the S1 includes:

[0067] S11, obtaining a reference image of the target area;

[0068] S12, estimating image noise using a Laplacian operator on the reference image;

[0069] S13, and using the Barron operator to calculate the MIG value of the reference image sub-area of ​​the calculation point;

[0070] S14, using a speckle evaluation method based on an SSSIG threshold to estimate an initial image sub-region size and a value range of the image sub-region size; wherein the initial image sub-region size and the value range of the image sub-region size are determined by an initial value of the image sub-region size, an upper bound of the image sub-region size, and a lower bound of the image sub-region size; the speckle evaluation method based on an SSSIG threshold is directly used for estimating the initial value of the image sub-region size and the upper bound of the image sub-region size; and the lower bound of the image sub-region size is set to a fixed value of pixels by default;

[0071] In this embodiment, the initial value of the image sub-area size and the upper SSSIG threshold are set to 2×10 4 and 2×10 6 In addition, the lower limit of the image sub-region size is set to 11 pixels by default to ensure sufficient grayscale changes within the image sub-region.

[0072] In order to achieve full automation of the method, this embodiment uses a speckle evaluation method based on the SSSIG threshold to estimate the initial image sub-region size and the value range of the image sub-region size. The speckle evaluation method can select and calculate a stable initial image sub-region size by calculating the SSSIG value of the reference image, effectively ensuring the accuracy of the initial displacement estimation. At the same time, the obtained image sub-region size range can automatically truncate unreasonable image sub-region sizes, thereby improving the stability and effectiveness of the algorithm.

[0073] S2, performing temporal matching and stereo matching DIC calculation based on the size of the initial image sub-area, thereby obtaining an initial displacement field, and estimating high-order displacement derivatives based on a pointwise least squares (PLS) method;

[0074] As a preferred embodiment, the S2 includes:

[0075] S21, performing temporal matching and stereo matching DIC calculation on the calculation points based on the size of the initial image sub-area, so as to obtain an initial displacement field, wherein the temporal matching uses a first-order shape function and the stereo matching uses a second-order shape function;

[0076] S22, fitting the local displacement fields near the calculation points respectively based on the Pointwise least squares (PLS) method; wherein the local least squares fitting method is implemented based on a second-order polynomial or a fourth-order polynomial, wherein the timing matching uses the second-order polynomial, and the stereo matching uses the fourth-order polynomial.

[0077] As a preferred implementation, the S22 may use a numerical differentiation method to replace the local least squares fitting method to estimate the high-order displacement derivatives.

[0078] S23, estimate higher-order displacement derivatives based on the fitted polynomial coefficients.

[0079] S3, calculating the optimal image sub-region size based on the high-order displacement derivative;

[0080] As a preferred implementation, S3 includes:

[0081] S31, according to the estimated parameters, the optimal image sub-region sizes for temporal matching and stereo matching are respectively calculated in two formulas; and the optimal image sub-region sizes for temporal matching and stereo matching are set to the calculated image sub-region sizes;

[0082] As a preferred implementation, the S31 is used to obtain the optimal image sub-area size for 3D-DIC timing matching and stereo matching, including:

[0083] (1) The random error caused by image noise, which plays a major role in the DIC matching process, and the systematic error of undermatching are combined into the following mean absolute error (MAE):

[0084]

[0085] Among them, e d is the undermatched system error, σ d is the random error caused by image noise;

[0086] (2) The theoretical formula for quantifying the DIC random error is:

[0087]

[0088] And there is a theoretical formula to quantify the systematic error of DIC undermatch:

[0089]

[0090] Substitute into formula (1) to calculate MAE d The calculation formula for the minimum value of the image sub-area size is used to obtain the optimal image sub-area size. Formulas (2) and (3) are the error estimation formulas for the horizontal displacement u, and the error formula for the vertical displacement v is similar. x and MIG y is the average grayscale gradient of the image in the horizontal and vertical directions, σ n is the standard deviation of the image noise.

[0091] Specifically, the total error using first-order shape function matching is:

[0092]

[0093] in, is the Laplace operator ( and denote the second-order partial derivative operators in the x-direction and y-direction respectively). Since MAE d,1st The function curve of the image sub-area size parameter M is U-shaped, so we only need to let Then solving this equation gives the optimal image subregion size M using the first-order shape function: 1st :

[0094]

[0095] in, The symbol represents the integer part of x.

[0096] Similarly, the total error of matching using second-order shape functions is:

[0097]

[0098] Where R and S are expressed as:

[0099]

[0100] Similarly, let MAE d,2nd The partial derivative of the image subregion size parameter M is 0, that is: Solving this equation yields the optimal image subregion size M using a second-order shape function: 2nd :

[0101]

[0102] Based on the obtained explicit calculation formula of the optimal image sub-area size, the present invention designs a 3D-DIC algorithm with adaptive selection of matching calculation parameters to achieve high-precision and high-efficiency 3D-DIC calculation. Using this explicit calculation formula, there is no need for iterative optimization calculation, thereby greatly reducing the amount of calculation and the complexity of algorithm implementation.

[0103] S32: If the calculated image sub-region size exceeds the set image sub-region range, truncation processing is performed according to the image sub-region range.

[0104] In this embodiment, the optimal image sub-area size for stereo matching and timing matching of the left and right cameras obtained in step S32 is as follows: Figure 3 shown.

[0105] S4, taking the initial displacement field in S2 as the initial value, combined with the optimal image sub-area size obtained in S3, based on the adaptive method and the conventional method, respectively optimize the displacement calculation results of the temporal matching and the stereo matching to obtain the corresponding image displacement, and obtain the final image displacement result.

[0106] As a preferred embodiment, the optimization algorithm is an Inverse Compositional Gauss-Newton (IC-GN) algorithm. Of course, those skilled in the art can easily imagine that other appropriate optimization algorithms can be used, all of which are within the scope of protection of the present invention.

[0107] S5, performing three-dimensional reconstruction based on the corresponding image displacement and combining system parameters of the simulation system, so as to obtain three-dimensional displacement results of all calculation points.

[0108] The process of the adaptive method proposed by the present invention is as follows: Figure 1 As shown, it mainly involves the adaptive calculation of temporal matching and stereo matching. The detailed process can be described as follows:

[0109] The first step is to estimate the parameters in the formula: first, use the Laplacian operator to estimate the image noise of the reference image, and use the Barron operator to calculate the mean intensity gradient (MIG) of the reference image of each calculation point. Then, use the speckle evaluation method based on the SSSIG threshold to estimate the initial image sub-area size and the upper limit of the image sub-area. It is recommended to set the SSSIG threshold for the initial value of the image sub-area size and the upper limit to 2×10 4 and 2×10 6 In addition, the lower limit of the image sub-region size is set to 11 pixels by default to ensure that the image sub-region contains sufficient grayscale changes.

[0110] The second step is to calculate the optimal image sub-region size. According to the parameters estimated in the previous step, they are substituted into the optimal image sub-region size calculation formulas for temporal matching and stereo matching respectively. If the calculated image sub-region size exceeds the set image sub-region range, it will be truncated to ensure that it is within a reasonable range.

[0111] The third step is to recalculate the displacement field by optimizing the displacement result of the DIC calculation: the displacement result of the DIC calculation is used as the initial value, and the optimal image sub-area size obtained by the above steps is used to optimize the displacement calculation results of stereo matching and temporal matching using the Inverse compositional Gauss-Newton (IC-GN) algorithm.

[0112] In the fourth step, based on the image displacements of temporal matching and stereo matching calculated by the adaptive method and the conventional method, three-dimensional reconstruction is performed using the system parameters of the simulation system to obtain the three-dimensional displacement field of all calculation points. Specific embodiment one:

[0114] Figure 2 The figure is a schematic diagram of the dual-camera 3D-DIC system simulation imaging, which simulates the complex non-uniform deformation of the specimen surface with only a horizontal sinusoidal displacement field. By applying a sinusoidal displacement field with an amplitude of 2 mm and a period of 200 mm, the simulation experiment obtained the left and right camera images of the specimen surface before and after deformation. In addition, random noise with a mean of 0 and a standard deviation of 3 was added to all images to simulate the image noise during camera acquisition.

[0115] The displacement of the simulated image is calculated using an adaptive method, where the detailed steps of stereo matching and temporal matching are as follows:

[0116] The first step is to estimate the relevant parameters in the formula. The Laplacian operator is used to estimate the image noise of the reference image, and the Barron operator is used to calculate the MIG value of the reference image sub-area of ​​the calculation point. The initial value and upper limit of the image sub-area size are directly estimated using the speckle evaluation method based on the SSSIG threshold. In this paper, the SSSIG threshold of the initial value and upper limit of the image sub-area size are set to 2×10 4 and 2×10 6 In addition, the lower limit of the image sub-region size is set to 11 pixels by default to ensure sufficient grayscale changes within the image sub-region.

[0117] The second step is to estimate the high-order displacement derivatives. The initial image sub-area size obtained in the previous step is used to try DIC calculations on the calculation points (first-order shape functions are used for time series matching and second-order shape functions are used for stereo matching). Then, the local displacement fields near the calculation points are fitted using the PLS method based on second-order polynomials or fourth-order polynomials (second-order polynomials are used for time series matching and fourth-order polynomials are used for stereo matching). Finally, the high-order displacement derivatives are calculated based on the fitted polynomial coefficients. Among them, the high-order displacement derivatives can also be directly solved using numerical differentiation.

[0118] The third step is to calculate the optimal image sub-area size. According to the estimated parameters, the optimal image sub-area sizes for temporal matching and stereo matching are calculated in two formulas. If the calculated image sub-area size exceeds the set image sub-area range, it is truncated according to the image sub-area range. This step obtains the optimal image sub-area sizes for stereo matching and temporal matching of the left and right cameras, as shown in Figure 3 shown.

[0119] In the fourth step, the result of the DIC calculation is used as the initial value, and the IC-GN algorithm is used to optimize the optimal image sub-area size obtained in the above steps to obtain the final image displacement result.

[0120] Based on the image displacements of temporal matching and stereo matching calculated by the adaptive method and the conventional method, the system parameters of the simulation system are used for 3D reconstruction, and the 3D displacement field of all calculation points is obtained, such as Figure 4 Finally, the strain field is extracted from the calculated three-dimensional displacement field using the PLS method, as shown in Figure 5 shown.

[0121] According to the parameters automatically selected by the adaptive method, the optimal image subregion size shows a periodic change. Specifically, when the deformation of the calculation area is large, the adaptive method tends to choose a smaller image subregion size to reduce the undermatching system error; while in the area with smaller deformation, choosing a larger image subregion size helps to reduce the random error.

[0122] Compared with conventional methods, the displacement and strain fields obtained by adaptive methods have less noise (see Figure 5 ), which shows that this method has higher measurement accuracy. Further comparison of the calculation results of the two methods shows that the present invention extracts Figure 6 The displacement and strain distribution in the horizontal direction are shown. The results show that the maximum displacement and strain obtained by the adaptive method are closer to the actual applied 2mm displacement, so it has a higher measurement resolution. Since the adaptive method can automatically select the optimal calculation parameters for each 3D-DIC calculation point, it can achieve 3D-DIC measurement with higher full-field measurement accuracy.

[0123] Therefore, the optimal matching parameter adaptive selection algorithm proposed in the first embodiment of the present invention has higher matching accuracy and stability, and is expected to achieve important application and promotion in the field related to three-dimensional digital images.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A three-dimensional digital image method based on adaptive calculation of optimal image sub-area size, characterized in that: include: S1, obtaining a reference image of the target area, estimating relevant parameters in an image parameter formula applied to the reference image, and estimating an initial image sub-area size and a value range of the image sub-area size based on a speckle evaluation method using an SSSIG threshold; S2, performing temporal matching and stereo matching DIC calculation based on the initial image sub-area size, thereby obtaining an initial displacement field, and estimating high-order displacement derivatives based on a local least squares fitting method; S3, calculating the optimal image sub-region size based on the high-order displacement derivative; S4, taking the initial displacement field in S2 as an initial value, combining with the optimal image sub-area size obtained in S3, optimizing the displacement calculation results of temporal matching and stereo matching based on an adaptive method and a conventional method to obtain corresponding image displacements, and obtaining a final image displacement result; S5, performing three-dimensional reconstruction based on the corresponding image displacement and combining system parameters of the simulation system, so as to obtain three-dimensional displacement results of all calculation points.

2. A three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 1, characterized in that: The S1 includes: S11, obtaining a reference image of the target area; S12, estimating image noise using a Laplacian operator on the reference image; S13, and using the Barron operator to calculate the MIG value of the reference image sub-area of ​​the calculation point; S14, using a speckle evaluation method based on an SSSIG threshold to estimate an initial image sub-region size and a value range of the image sub-region size; wherein the initial image sub-region size and the value range of the image sub-region size are determined by an initial value of the image sub-region size, an upper bound of the image sub-region size, and a lower bound of the image sub-region size; the speckle evaluation method based on an SSSIG threshold is directly used for estimating the initial value of the image sub-region size and the upper bound of the image sub-region size; and the lower bound of the image sub-region size is set to a fixed value of pixels by default.

3. A three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 2, characterized in that: The S2 includes: S21, performing temporal matching and stereo matching DIC calculation on the calculation points based on the size of the initial image sub-area, so as to obtain an initial displacement field, wherein the temporal matching uses a first-order shape function and the stereo matching uses a second-order shape function; S22, fitting the local displacement fields near the calculation points respectively based on the local least squares fitting method; wherein the local least squares fitting method is implemented based on a second-order polynomial or a fourth-order polynomial, wherein the timing matching uses the second-order polynomial, and the stereo matching uses the fourth-order polynomial. S23, estimate higher-order displacement derivatives based on the fitted polynomial coefficients.

4. A three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 3, characterized in that: The S22 may estimate the high-order displacement derivatives by replacing the local least squares fitting method with a numerical differentiation method.

5. The three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 3, characterized in that: The S3 includes: S31, according to the estimated parameters, the optimal image sub-region sizes for temporal matching and stereo matching are respectively calculated in two formulas; and the optimal image sub-region sizes for temporal matching and stereo matching are set to the calculated image sub-region sizes; S32: If the calculated image sub-region size exceeds the set image sub-region range, truncation processing is performed according to the image sub-region range.

6. A three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 5, characterized in that: The S31 is used to obtain the optimal image sub-area size for 3D-D1C timing matching and stereo matching, including: (1) The random error caused by image noise, which plays a major role in the DLC matching process, and the systematic error of undermatching are combined into the following mean absolute error (MAE): Among them, e d is the undermatched system error, σ d is the random error caused by image noise; (2) Substitute the existing theoretical formula (2) for quantifying the random error of DIC and the existing theoretical formula (3) for quantifying the systematic error of DIC undermatching into formula (1) to calculate MAE d The calculation formula for the minimum value of the image sub-area size is used to obtain the optimal image sub-area size. Formulas (2) and (3) are the error estimation formulas for the horizontal displacement u, and the error formula for the vertical displacement v is similar. x and MIG y is the average grayscale gradient of the image in the horizontal and vertical directions, σ n is the standard deviation of the image noise; where formula (2) and formula (3) are respectively: Specifically, the total error using first-order shape function matching is: in, is the Laplace operator; and Represent the second-order partial derivative operators in the x-direction and y-direction respectively; due to MAE d，1st The function curve of the image sub-area size parameter M is U-shaped, so we only need to let Then solving this equation gives the optimal image subregion size M using the first-order shape function: 1st : in, The symbol means taking the integer part of x; Similarly, the total error of matching using second-order shape functions is: Among them, R and S are expressed as: Similarly, let MAE d，2nd The partial derivative of the image subregion size parameter M is 0, that is: Solving this equation yields the optimal image subregion size M using a second-order shape function: 2nd :

7. A three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 1, characterized in that: The optimization algorithm is the reverse combined Gauss-Newton algorithm.

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