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

Abstract

The application discloses a three-dimensional digital image correlation (3D-DIC) method based on optimal image subregion size adaptive calculation, comprising the following steps: estimating initial image subregion size and the value range of the image subregion size based on a speckle evaluation method of image subregion answer gradient square sum (SSSIG) threshold; obtaining an initial displacement field based on initial image subregion size for attempt DIC calculation of time sequence matching and stereo matching, and estimating high-order displacement derivatives based on a local least square fitting method; calculating optimal image subregion size based on the high-order displacement derivatives and speckle correlation parameters; taking the initial displacement field as an initial value, and combining optimal image subregion size of time sequence matching and stereo matching to obtain a final image displacement result through optimized calculation; and verifying that the adaptive algorithm used in the application has higher measurement accuracy than conventional three-dimensional digital image correlation methods based on simulated image data generated by a simulated stereo vision system.

Description

TECHNICAL FIELD

[0001] The present application 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 for measuring three-dimensional topography, displacement and deformation of object surface, and more particularly to a three-dimensional digital image correlation method based on adaptive calculation of optimal image sub-region size. BACKGROUND

[0002] Three-dimensional digital image correlation (3D-DIC) is a non-contact optical measurement method that can measure full-field topography, displacement and deformation of materials and structures (including biological tissues), and has been widely used in the fields of solid mechanics, materials science, aerospace, civil engineering, etc. This method can obtain full-field three-dimensional topography, displacement and strain of the surface of the test piece by comparing the digital images of the measured object before and after deformation collected by a stereo imaging system and combining the principles of computer stereo vision measurement. 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 series matching between the same camera images are needed. Whether it is stereo matching or time series matching, non-linear optimization of the correlation function is needed, and appropriate matching parameters, mainly including image sub-region size and shape function, need to be selected. However, existing 3D-DIC software often leaves the selection of parameters to the user to manually set, and lacks clear guidance, making it difficult to obtain optimal calculation results. Studies have shown that the selection of these matching parameters needs to fully consider the speckle quality in the experiment and the local deformation of the test piece, so the optimal calculation parameters required by the stereo matching and time series matching steps of each calculation point in actual 3D-DIC calculation are not consistent, and the optimal calculation parameters need to be set individually for each calculation point. Therefore, how to realize the adaptive selection of optimal calculation parameters has become a key problem and an important challenge for realizing adaptive and accurate 3D-DIC measurement.

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

[0004] However, the selection of the optimal image sub-region size needs to consider both the local speckle quality and the local deformation size. The speckle quality evaluation method only considers the local speckle quality, that is, only the random error in matching is optimized, and therefore the method may fail for non-uniform complex deformation measurement, and cannot select the optimal parameters in the complex deformation condition. This means that in actual application, only relying on the speckle quality evaluation method may not achieve the required matching accuracy and stability.

[0005] The existing method may fail in the face of complex deformation conditions and cannot simultaneously consider the local speckle quality and the local deformation, which limits the practicality and reliability of the parameter adaptive selection method in actual application. SUMMARY

[0006] The present application aims at several defects in the prior art and proposes a precise three-dimensional digital image correlation method based on adaptive calculation of optimal image sub-region size, which can automatically select an adaptive precise three-dimensional digital image correlation algorithm of two optimal matching parameters. The inventive concept is that in 3D-DIC, in order to realize three-dimensional reconstruction and three-dimensional displacement tracking of the calculation points, stereo matching between different cameras and time sequence matching of the same camera need to be performed. Due to the angle difference between different cameras, the local deformation of stereo matching is more complex, and therefore a more complex second-order shape function needs to be used. The time sequence matching between the same cameras only needs to match the deformation of the specimen at the same angle, and the local deformation of the time sequence matching is generally simpler, and therefore a first-order shape function can be generally used. Based on this, the present application uses a fixed first-order shape function and a second-order shape function in time sequence matching and stereo matching, respectively, to better adapt to the needs of different matching scenarios.

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

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

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

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

[0011] S4, using the initial displacement field in S2 as an initial value, combining the optimal image sub-region size obtained in S3, and based on adaptive and conventional methods, respectively optimizing the displacement calculation results of the time series matching and the stereo matching to obtain corresponding image displacements, and obtaining the final image displacement results;

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

[0013] Preferably, S1 comprises:

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

[0015] S12, using a Laplacian operator to estimate the image noise of the reference image;

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

[0017] S14, estimating the initial image sub-region size and the value range of the image sub-region size based on the speckle evaluation method based on the SSSIG threshold; wherein the initial image sub-region size and the value range of the image sub-region size are determined by the initial value of the image sub-region size, the upper limit of the image sub-region size and the lower limit of the image sub-region size; the initial value of the image sub-region size and the upper limit of the image sub-region size are directly estimated using the speckle evaluation method based on the SSSIG threshold; the lower limit of the image sub-region size is a fixed number of pixels by default;

[0018] Preferably, S2 comprises:

[0019] S21, based on the initial image sub-region size, performing an attempt DIC calculation of time series matching and stereo matching of the calculation point, thereby obtaining an initial displacement field, wherein the time series matching uses a first-order shape function, and the stereo matching uses a second-order shape function;

[0020] S22, based on a local least squares fitting method, fitting the local displacement field near the calculation point; wherein the local least squares fitting method is based on a second-order polynomial or a fourth-order polynomial, wherein the time series matching uses the second-order polynomial, and the stereo matching uses the fourth-order polynomial.

[0021] S23, estimating the high-order displacement derivative based on the fitted polynomial coefficients.

[0022] Preferably, S22 can replace the local least squares fitting method to estimate the high-order displacement derivative based on a numerical differentiation method.

[0023] Preferably, S3 comprises:

[0024] S31, according to the estimated parameters, the two formulae are brought into to calculate the optimal image sub-region size of time sequence matching and stereo matching respectively; the optimal image sub-region size of time sequence matching and stereo matching is set as the calculated image sub-region size;

[0025] S32, if the calculated image sub-region size exceeds the set image sub-region range, the image sub-region range is used for truncation processing.

[0026] Preferably, the S31 is used for obtaining the optimal image sub-region size for 3D-DIC time sequence matching and stereo matching, and includes:

[0027] (1) the random error caused by image noise and the system error of undermatching in the DIC matching process are synthesized into the following mean absolute error (MAE):

[0028]

[0029] wherein e d is the system error of undermatching, and σ d is the random error caused by image noise;

[0030] (2) the existing theoretical formula (2) quantifying the DIC random error and the existing theoretical formula (3) quantifying the DIC system error of undermatching are brought into the formula (1) to calculate the MAE d The calculation formula of the minimum value of the image sub-region size is obtained to obtain the optimal image sub-region size. The formula (2) and (3) are the error estimation formula of the horizontal displacement u, and the error formula of the vertical displacement v is similar. Wherein MIG x and MIG y are the average gray gradient of the image in the horizontal direction and the vertical direction, and σ n is the standard deviation of the image noise; wherein the formula (2) and the formula (3) are respectively:

[0031]

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

[0033]

[0034] wherein, is the Laplace operator; and respectively represent the second-order partial derivative operator in the x direction and the y direction; since MAE d,1stThe function curve of the image sub-region size parameter M is U-shaped, so only need to let Then solve the equation to obtain the optimal image sub-region size M using the first-order shape function 1st :

[0035]

[0036] wherein, The symbol represents the integer part of x;

[0037] Similarly, the total error matched using the second-order shape function is:

[0038]

[0039] wherein, R and S represent:

[0040]

[0041] Similarly, let MAE d,2nd The partial derivative of the image sub-region size parameter M is 0, that is: Solve the equation to obtain the optimal image sub-region size M using the second-order shape function 2nd :

[0042]

[0043] Preferably, the adaptive algorithm is a reverse combination Gauss-Newton algorithm.

[0044] The method of the present application has the following advantages:

[0045] (1) The present application fully considers the deficiencies of the speckle quality evaluation method in the prior art, 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 parameter, thereby improving the accuracy and precision of the matching.

[0046] (2) The present application is based on the principle of minimizing the synthetic error, and uses the synthetic 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 the adaptive algorithm unnecessary to perform iterative calculation, thereby greatly improving the calculation efficiency, reducing the calculation time and implementation complexity.

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

[0048] (4) The present invention takes into account the characteristics of temporal matching and stereo matching, directly employing first-order shape functions and second-order shape functions for temporal matching and stereo matching, respectively. This effectively improves the computational efficiency of parameter selection and further simplifies the algorithmic complexity. These characteristics make the present invention more applicable and reliable in practical applications, and can provide a more effective and reliable solution for selecting matching parameters in 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 adaptively selected optimal calculation parameters.

[0051] Automated representation: The present invention adopts 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, thereby effectively ensuring the accuracy of the initial displacement estimation and automatically truncating unreasonable image sub-region sizes, thereby improving the stability and effectiveness of the algorithm.

[0052] Simple representation: 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 related technologies, the following briefly introduces the drawings required for use in the specific embodiments or related technical descriptions. 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 any creative work.

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

[0055] Figure 2 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 by the embodiment of the present invention. Figure 3 (a) Schematic diagram of the optimal image sub-region size for stereo matching; Figure 3 (b) Schematic diagram of the optimal image sub-region size for left camera temporal matching; Figure 3 (c) Schematic diagram of the optimal image sub-region size for timing matching of the right camera.

[0057] Figure 4 The displacement field calculated by the conventional 3D-DIC method and the adaptive method according to the embodiment of the present application.

[0058] Figure 5 The strain field calculated by the conventional 3D-DIC method and the adaptive method according to the embodiment of the present application.

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

[0060] The technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

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

[0062] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0063] Embodiment one

[0064] The present embodiment provides a three-dimensional digital image method based on optimal image sub-region size adaptive calculation, comprising:

[0065] S1, obtaining a reference image of a target region, and estimating an initial image sub-region size and a range of the image sub-region size based on a speckle evaluation method of SSSIG threshold;

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

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

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

[0069] S13, and calculating the MIG value of the reference image sub-region of the calculation point using a Barron operator;

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

[0071] In this embodiment, the SSSIG threshold of the initial value and the upper limit of the image sub-region size are set to 2x10 4 and 2x10 6 respectively. In addition, the lower limit of the image sub-region size is 11 pixels by default to ensure sufficient gray scale variation in the image sub-region.

[0072] In order to realize full automation of the method, the speckle evaluation method of SSSIG threshold is used to estimate the initial image sub-region size and the range of the image sub-region size in this embodiment. This speckle evaluation method can select 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 range of the image sub-region size can automatically cut off unreasonable image sub-region size, improving the stability and effectiveness of the algorithm.

[0073] S2, performing time series matching and stereo matching attempt DIC calculation based on the initial image sub-region size, 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 comprises:

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

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

[0077] As a preferred embodiment, the S22 can use a numerical differentiation method to replace the PLS method to estimate high-order displacement derivatives.

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

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

[0080] As a preferred embodiment, the S3 includes:

[0081] S31, calculating optimal image sub-region sizes for time matching and stereo matching respectively according to the estimated parameters, and setting the optimal image sub-region sizes for time matching and stereo matching as the calculated image sub-region size;

[0082] As a preferred embodiment, the S31 is used to obtain optimal image sub-region sizes for 3D-DIC time matching and stereo matching, and includes:

[0083] (1) the random error caused by image noise and the system error caused by under-matching in the DIC matching process are combined into the following Mean absolute error (MAE):

[0084]

[0085] wherein e d is the system error caused by under-matching, and σ d is the random error caused by image noise;

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

[0087]

[0088] and the existing theoretical formula for quantifying the DIC system error caused by under-matching is:

[0089]

[0090] Substitute equation (1) into MAE d The formula for the minimum value of the image sub-region size, thus obtaining the optimal image sub-region size, is given by equations (2) and (3) for the error estimation formula of the horizontal displacement u, and the error formula of the vertical displacement v is similar. Where, MIG x and MIG y are the average gray level gradients in the horizontal and vertical directions of the image, and σ n is the standard deviation of the image noise.

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

[0092]

[0093] Where, is the Laplace operator ( and represent the second-order partial derivative operators in the x and y directions, respectively). Since MAE d,1st The function curve of the image sub-region size parameter M is U-shaped, so it is only necessary to let Then solving the equation can obtain the optimal image sub-region size M 1st using a first-order shape function match:

[0094]

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

[0096] Similarly, the total error using a second-order shape function match is:

[0097]

[0098] Where, R and S are represented as:

[0099]

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

[0101]

[0102] Based on the explicit calculation formula of the optimal image sub-region size, the application designs a 3D-DIC algorithm with adaptive selection of matching calculation parameters to realize high-precision and high-efficiency 3D-DIC calculation. Using the explicit calculation formula, iteration optimization calculation is not needed, so that the calculation amount and the complexity of algorithm implementation are greatly reduced.

[0103] In S32, if the calculated image sub-region size exceeds the set image sub-region range, the image sub-region range is used for truncation processing.

[0104] In the embodiment, the optimal image sub-region size of the stereo matching and the respective time sequence matching of the left and right cameras obtained in S32 is as shown in the table. Figure 3

[0105] In S4, the initial displacement field in S2 is taken as an initial value, the optimal image sub-region size obtained in S3 is combined, and the adaptive method and the conventional method are used to respectively optimize the calculation results of the time sequence matching and the stereo matching to obtain corresponding image displacements, so that the final image displacement result is obtained.

[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 think of other appropriate optimization algorithms, which are all within the protection scope of the application.

[0107] In S5, based on the corresponding image displacement, the system parameters of the simulation system are combined to perform three-dimensional reconstruction, so that the three-dimensional displacement result of all calculation points is obtained.

[0108] The adaptive method flowchart proposed in the application is as shown in the table. Figure 1

[0109] Firstly, the Laplacian operator is used to estimate the image noise of the reference image, and the Barron operator is used to calculate the mean intensity gradient (MIG) of the reference image of each calculation point. Then, the initial image sub-region size and the upper limit of the image sub-region are estimated by using the speckle evaluation method based on the SSSIG threshold. It is recommended that the SSSIG threshold for setting the initial value and the upper limit of the image sub-region size is 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 gray scale changes.

[0110] ​​Second step, optimal image sub-region size calculation. According to the estimated parameters in the previous step, they are respectively substituted into the optimal image sub-region size calculation formula of time series matching and stereo matching. If the calculated image sub-region size exceeds the set image sub-region range, it will be truncated to ensure it within a reasonable range.

[0111] Third step, displacement field re-optimization calculation: the displacement result of DIC calculation is used as the initial value, and the optimal image sub-region size obtained by the above steps is used to optimize the calculation results of stereo matching and time series matching displacement calculation respectively using inverse composition Gauss-Newton (IC-GN) algorithm.

[0112] Fourth step, based on the adaptive method and the conventional method, the image displacement of time series matching and stereo matching is calculated, and the system parameters of the simulation system are used for three-dimensional reconstruction to obtain the three-dimensional displacement field of all calculation points. Embodiment one:

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

[0115] The adaptive method is used to calculate the displacement of the simulation image, and the detailed steps of stereo matching and time series matching are as follows:

[0116] First step, estimation of related parameters in the formula. The Laplacian operator is used to estimate the image noise for the reference image, and the Barron operator is used to calculate the MIG value of the reference image sub-region of the calculation point. The initial value and the upper limit of the image sub-region size are directly calculated using the speckle evaluation method based on the SSSIG threshold, and the SSSIG threshold of the initial value and the upper limit of the image sub-region size in this paper is 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 gray scale variation in the image sub-region.

[0117] Second step, high order displacement derivative estimation. Using the initial image sub-region size obtained in the previous step, the DIC calculation is performed for the calculation point (first order polynomial function is used for time series matching and second order polynomial function is used for stereo matching), then the local displacement field near the calculation point is fitted by the PLS method based on the second order polynomial or fourth order polynomial (second order polynomial is used for time series matching and fourth order polynomial is used for stereo matching), and finally the high order displacement derivatives are calculated according to the fitted polynomial coefficients. The high order displacement derivatives can also be directly solved by numerical differentiation.

[0118] Third step, optimal image sub-region size calculation. According to the estimated parameters, the optimal image sub-region sizes for time series matching and stereo matching are calculated by using two formulas respectively. If the calculated image sub-region size exceeds the set image sub-region range, the truncation processing is performed according to the image sub-region range. The optimal image sub-region sizes for stereo matching and left and right camera time series matching obtained in this step are shown in FIG. 3. Figure 3

[0119] Fourth step, using the results of the trial DIC calculation as the initial value, combining the optimal image sub-region size obtained in the above step, the IC-GN algorithm is used for optimization calculation to obtain the final image displacement results.

[0120] The image displacements of time series matching and stereo matching calculated based on the adaptive method and the conventional method are used for three-dimensional reconstruction by using the system parameters of the simulation system, and the three-dimensional displacement fields of all calculation points are obtained, as shown in FIG. 4. Figure 4 Finally, the strain fields are extracted from the calculated three-dimensional displacement fields by using the PLS method, as shown in FIG. 5. Figure 5

[0121] According to the parameters automatically selected by the adaptive method, the optimal image sub-region size presents a periodic variation characteristic. Specifically, when the deformation of the calculation region is large, the adaptive method tends to select a smaller image sub-region size to reduce the under-matching system error; and in the region with smaller deformation, selecting a larger image sub-region size is helpful to reduce the random error.

[0122] Compared with the conventional method, the displacement field and the strain field obtained by the adaptive method have smaller noise (see FIG. 6), Figure 5 which indicates that the method has higher measurement accuracy. Further comparing the calculation effects of the two methods, the horizontal direction displacement and strain distribution shown in FIG. 7 are extracted. Figure 6 The results show that the maximum displacement and strain obtained by the adaptive method are closer to the true displacement of 2mm, and therefore the adaptive method has higher measurement resolution. Since the adaptive method can automatically select the optimal calculation parameters for each 3D-DIC calculation point, the 3D-DIC measurement with higher measurement accuracy can be realized.

[0123] ​​Therefore, the optimal matching parameter adaptive selection algorithm provided by the embodiment one of the present application has higher matching accuracy and stability, and is expected to achieve important application and popularization in the field of three-dimensional digital image correlation.

[0124] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A three-dimensional digital image method based on adaptive calculation of optimal image sub-region 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 based on the initial image sub-region size to attempt DIC calculation, 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, using the initial displacement field in S2 as an initial value, combined with the optimal image sub-region size obtained in S3, and 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, thereby 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 to obtain three-dimensional displacement results of all calculation points; The S3 includes: S31, based on the estimated parameters, the optimal image sub-region sizes for temporal matching and stereo matching are calculated in two formulas respectively; and the optimal image sub-region sizes for temporal matching and stereo matching are set as the calculated image sub-region sizes; S32, if the calculated image sub-region size exceeds the set image sub-region range, performing truncation processing according to the image sub-region range; The S31 is used to obtain the optimal image sub-area size for 3D-DIC temporal matching and stereo matching, including: (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): (1) in, is the undermatched system error, 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 undermatch into formula (1) to calculate The calculation formula for the minimum value of the image sub-area size to obtain the optimal image sub-area size; Formulas (2) and (3) are the horizontal displacement The error estimation formula of vertical displacement The error formula is similar to that of ; where and is the average grayscale gradient of the image in the horizontal and vertical directions, is the standard deviation of image noise; where formula (2) and formula (3) are: (2): (3)； Specifically, the total error of matching using first-order shape functions is: (4) in, is the Laplace operator; and Respectively Direction and The second-order partial derivative operator in the direction; due to About image subarea size parameters The function curve is U-shaped, so we only need to make , and then solving this equation gives the optimal image subregion size using the first-order shape function : (5) in, Symbol indicates the The integer part of ; Similarly, the total error of matching using second-order shape functions is: (6) in, and Expressed as: (7) Similarly, About image subarea size parameters The partial derivative is 0, that is: , solving this equation yields the optimal image subregion size using a second-order shape function : (8)。 2. The three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 1, characterized in that: Said S1 comprises: 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-region 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 initial value of the image sub-region size and the upper bound of the image sub-region size are estimated directly using the speckle evaluation method based on the SSSIG threshold; and the lower bound of the image sub-region size is set to a fixed value of pixels by default.

3. The 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 calculations on the calculation points based on the size of the initial image sub-area, thereby obtaining 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 a 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 time series 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. The three-dimensional digital image method based on adaptive calculation of optimal image sub-region size according to claim 3, characterized in that: The S22 estimates 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 optimization algorithm is an inverse combined Gauss-Newton algorithm.

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