CN111369549A - Digital image deformation characterization method and device, electronic equipment and medium - Google Patents
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Abstract
The invention discloses a digital image deformation characterization method and device for regulating and controlling image quality and deformation gradient, electronic equipment and a medium. The digital image deformation characterization method comprises the following steps: providing speckle images before and after deformation; aiming at speckle images before and after deformation, determining the minimum value of the unit or sub-area size through the change of the Hessian matrix condition number; according to the minimum value of the unit or sub-area size, roughly calculating a displacement field by searching or appointing initial grid or sub-area distribution through whole pixels; according to the displacement field and the tangent vector correlation function C (r) ═ t(s) t (s + r)min＝C0Determining the maximum value of the unit or sub-area size; judging whether the convergence condition is satisfied according to the determined maximum value of the unit or sub-area size
Or i is more than N, i is iteration times, N is cycle times, if the iteration times and the cycle times are met, the current displacement field is output, and digital image deformation characterization of image quality and deformation gradient regulation is realized. The method is suitable for complex deformation conditions and image quality, has higher accuracy and accuracy, and is easy to use.
Description
Technical Field
The invention relates to the technical field of optical measurement, in particular to a digital image deformation characterization method and device for regulating and controlling image quality and deformation gradient, electronic equipment and a medium.
Background
The digital speckle correlation method is an optical measurement method for obtaining a corresponding deformation field by comparing speckle information contained in speckle images before and after deformation. The method has the advantages of no damage to the tested piece, easy operation, adaptability to severe testing environments (such as high temperature and high pressure) and the like, and is widely applied to the fields of aerospace, biomedicine and the like.
The digital speckle correlation method can be divided into local digital speckle correlation and global digital speckle correlation, wherein the local digital speckle correlation is to divide a series of independent sub-regions in an image, and has higher calculation efficiency; the global digital speckle correlation is to divide a series of units connected by nodes in an image, so that the continuity of a displacement field can be ensured. The cell or sub-region size is an important parameter related to digital speckle, and directly influences the accuracy of a calculation result. Because the speckle images used are different, the displacement field to be measured is varied, and the selection of the unit or sub-area size does not have a uniform standard at present, and depends on the experience of a user.
Especially when the nonlinear displacement field is measured, if the size of the unit or the sub-area is too large, the shape function of the unit or the sub-area cannot accurately depict the complex deformation in the unit or the sub-area, and further the calculation error is increased. If the cell or sub-region size is too small, the speckle information within the cell or sub-region is too small, causing an increase in random errors. The dual constraints of deformation and image quality make the calculation more sensitive to the value of the cell or sub-region size.
In addition, in practical applications, there are cases where the image quality of different regions is significantly different. For example: the different components of the composite material have different adhesion capacities of paint and ink; or different components of the composite material have different light absorption capacities, so that the amount of the doped fluorescent particles excited is different. Not only is the selection of cell or sub-region sizes more difficult, but even a uniform distribution of cell or sub-region sizes will be difficult to meet.
The above problems limit the further application of digital speckle correlation methods to complex problems.
Disclosure of Invention
Aiming at the defects of the existing digital speckle correlation method, the invention provides a digital image deformation characterization method, a digital image deformation characterization device, electronic equipment and a medium for regulating and controlling image quality and deformation gradient.
According to a first aspect of the embodiments of the present invention, there is provided a digital image deformation characterization method for image quality and deformation gradient regulation, the method including:
step 1: providing a speckle image before deformation and a speckle image after deformation;
step 2: determining the minimum value L of the size of a unit or a subarea according to the change of the condition number of a Hessian (Hessian) matrix aiming at the speckle image before deformation and/or the speckle image after deformationmin；
And step 3: according to the minimum value L of the cell or sub-region sizeminRoughly calculating a displacement field by searching or specifying initial grid or subregion distribution by integral pixels;
and 4, step 4: according to the displacement field and the tangent vector correlation function C (r) ═ t(s) t (s + r)min＝C0Determining the maximum value L of the cell or sub-region sizemaxWhere t(s) is the tangent to the curve or plane at s, C0Is a critical value;
and 5: according to a determined maximum value L of the cell or sub-region sizemaxJudging whether the convergence condition is satisfied
Or i is more than N, i is iteration times, N is cycle times, if the convergence condition is met, the current displacement field is output, and the digital image deformation characterization of image quality and deformation gradient regulation is realized.
In the above scheme, the speckle image after deformation in step 1 is obtained by moving the speckle image before deformation by a certain displacement, and a corresponding displacement field can be obtained by comparing the difference between the speckle image after deformation and the speckle image before deformation.
In the above scheme, the minimum value L of the unit or sub-region size is determined in step 2min，
According to the image quality of the speckle image, the speckle image before deformation is adopted, and the size of a unit or a sub-area which enables the Hessian condition number to be increased sharply is used as the minimum value of the size of the unit or the sub-area through the change of the Hessian matrix condition number; or
According to the image quality of the speckle image, the deformed speckle image is adopted, and the size of a unit or a sub-area which enables the Hessian condition number to be increased sharply is used as the minimum value of the size of the unit or the sub-area through the change of the Hessian matrix condition number; or
According to the image quality of the speckle images, the speckle images before deformation and the speckle images after deformation are respectively adopted, the minimum value of the size of a unit or a sub-area is respectively determined, and then the average value is taken as the minimum value of the size of the unit or the sub-area.
In the above scheme, the unit or the sub-area in step 2 is a triangle or a quadrangle.
In the above scheme, the speckle image before deformation and the speckle image after deformation need to be used when calculating the displacement field in step 3, and the speckle image before deformation and the speckle image after deformation correspond to one displacement field.
In the above scheme, the displacement field is calculated in step 3 by using a global digital speckle correlation method or a local digital speckle correlation method.
In the above scheme, the step 5 further includes: if the convergence condition is not met, reassigning the grid or sub-area distribution according to the strain gradient and the image quality of each point, executing digital speckle correlation calculation, and returning to execute the step 4 and the step 5 until the convergence condition is met.
According to a second aspect of the embodiments of the present invention, there is provided a digital image deformation characterization apparatus for image quality and deformation gradient regulation, the apparatus including:
the image providing module is used for providing a speckle image before deformation and a speckle image after deformation;
a first processing module, which is used for determining the minimum value L of the unit or sub-area size according to the change of the condition number of the Hessian matrix aiming at the speckle image before deformation and/or the speckle image after deformationmin；
Second oneA processing module for minimizing L according to the cell or sub-region sizeminRoughly calculating a displacement field by searching or specifying initial grid or subregion distribution by integral pixels;
a third processing module for obtaining a correlation function C (r) ═ t(s) t (s + r) according to the displacement field and the tangent vectormin＝C0Determining the maximum value L of the cell or sub-region sizemaxWhere t(s) is the tangent to the curve or plane at s, C0Is a critical value; and
a digital image deformation characterization module for determining the maximum value L of the cell or sub-region sizemaxJudging whether the convergence condition is satisfied
Or i is more than N, i is iteration times, N is cycle times, if the convergence condition is met, the current displacement field is output, and the digital image deformation characterization of image quality and deformation gradient regulation is realized.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus including: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the above-described digital image deformation characterization method.
According to a fourth aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing computer-executable instructions, which when executed, are configured to implement the above-mentioned digital image deformation characterization method.
Compared with the prior art, the digital image deformation characterization method, device, electronic equipment and medium for regulating and controlling the image quality and the deformation gradient, provided by the invention, can adapt to complex deformation conditions and image quality, have higher accuracy and precision, and are easy to use.
Drawings
For a more complete understanding of the present invention, and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description serve to explain the principles of the invention. Wherein:
fig. 1 schematically shows a flow chart of a method for digital image deformation characterization with image quality and deformation gradient regulation according to an exemplary embodiment of the present invention.
Fig. 2A schematically illustrates a speckle image before deformation according to an exemplary embodiment of the invention.
Fig. 2B schematically illustrates a speckle image after deformation according to an exemplary embodiment of the invention.
Fig. 3 schematically shows the displacement field distribution in the X-direction in an exemplary embodiment according to the present invention.
FIG. 4 schematically illustrates a grid for computing in accordance with an exemplary embodiment of the invention.
FIG. 5A is a schematic diagram illustrating an error mean comparison of the method of the present invention and a conventional method according to an exemplary embodiment of the present invention.
FIG. 5B is a schematic diagram showing a comparison of standard deviation error of the method of the present invention with a conventional method in accordance with an exemplary embodiment of the present invention.
FIG. 6 schematically illustrates a block diagram of an image quality and deformation gradient conditioned digital image deformation characterization apparatus, according to an exemplary embodiment of the present invention;
fig. 7 schematically shows a block diagram of an electronic device according to an exemplary embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. It is to be understood that such description is merely illustrative and not intended to limit the scope of the present invention. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
In this embodiment, a simulated speckle image is used, the image size is 400 pixels × 400 pixels, the left part of the image contains 400 × 200 × 0.02 speckle grains, the right part of the image contains 400 × 200 × 0.06 speckle grains, the speckle grains are circular with the diameter of 1.2 pixels, the image has no noise, the displacement field is a single sinusoidal displacement field, U is 5Sin (2 pi x/300) and V is 0, the displacement field in the white box in FIG. 2A is calculated, a global digital speckle correlation method is used, the error function is a zero mean normalized least square distance correlation function, the cell type is a three-node triangular cell, and the iterative method is Newton-Larson iteration, the specific steps are as follows:
step 1: providing a speckle image before deformation and a speckle image after deformation;
as shown in fig. 2A and 2B, fig. 2A is a speckle image before deformation, and fig. 2B is a speckle image after deformation; the deformed speckle image is obtained by moving the speckle image before deformation by a certain displacement, and the corresponding displacement field can be obtained by comparing the difference between the deformed speckle image and the speckle image before deformation.
Step 2: determining the minimum value L of the size of a unit or a subarea according to the change of the condition number of a Hessian (Hessian) matrix aiming at the speckle image before deformation and/or the speckle image after deformationmin：
When the minimum value of the cell size is determined, it is acceptable to use either the image before the deformation or the image after the deformation according to the image quality of the speckle image, or to determine the minimum value according to both of them and then to take the average value.
In mathematics, a Hessian matrix (Hessian matrix or Hessian) is a square matrix composed of second-order partial derivatives of a multivariate real-valued function, and the Hessian matrix is applied to a large-scale optimization problem solved by a newton method.
As the cell size decreases, the information contained within the cell decreases, with a consequent increase in Hessian, which in turn leads to an increase in computational error. The cell size that drastically increases the Hessian condition number can be taken as the minimum of the cell size. In this embodiment, the image quality is determined using the pre-deformation image, and the cell size minimum L is determined using an L corner discovery algorithm in a regularization tool box min10 pixels.
And step 3: according to the minimum value L of the cell or sub-region sizeminThe displacement field is roughly calculated by integer pixel search or by specifying the initial grid or sub-area distribution:
when calculating the displacement field, the speckle image before deformation and the speckle image after deformation are needed, and the two images correspond to one displacement field;
the deformed speckle image is obtained by moving the speckle image before deformation for a certain displacement, and a corresponding displacement field can be obtained by solving according to the speckle image before deformation and the deformed speckle image.
Here with Lmax＝2LminThe grid is divided as a cell size maximum of 20 pixels and the displacement field is calculated using a digital speckle correlation algorithm. The unit sizes of different positions satisfy that:
and:
β(X，Y)＝c1Q(X，Y)/Qmax+c2G(X，Y)/Gmax(2)
wherein
For describing the image quality at the point (X, Y), the larger the Q value indicates the better the image quality of the point, and a smaller size unit may be used. W is a Gaussian window function, (X)i，Yi) All the whole pixels in the gaussian window are in the shape of a circle with a radius of 5 pixels.
fX(Xi，Yi) And fX(Xi，Yi) Are respectively a point (X)i，Yi) The gradient of the gray scale along the X and Y directions.
For describing the strain gradient at point (X, Y), locations with smaller G values may use larger sized cells. E is the green strain tensor. c. C1And c2C is taken as the corresponding weighting coefficient, indicating the degree of influence of the strain gradient and the image quality on the calculation, wherein the degrees of influence are considered to be equivalent1＝c2Since the displacement field is not known at this time, only the first term of β (X, Y) is considered.
And 4, step 4: according to the displacement field obtained in the last step and the tangent vector correlation function C (r) ═ (t(s) · t (s + r))min＝C0Determining the maximum value L of the cell or sub-region sizemaxWhere t(s) is the tangent to the curve or plane at s, C0Critical value: when the size of the unit or the sub-area is too large, the shape function of the unit or the sub-area cannot accurately approximate the complex displacement field in the unit or the sub-area, so that the maximum value of the size of the unit or the sub-area is determined based on the displacement field instead of the speckle image.
In this embodiment, the description of the displacement curve by the shape function is equivalent to approximating the curve using a series of straight line segments. When the tangent vectors t(s) and t (s + r) are on the same straight line, c (r) is 1, which indicates that the displacement curve between the two points can be completely replaced by a straight line; as the length r of the straight line segment increases, c (r) becomes smaller and smaller, and the accuracy of approximating a curve using it becomes lower and lower. When the correlation function is less than the threshold value C0Then, the maximum value L of the cell size is obtainedmax29 pixels, here take C0＝0.997。
And 5: according to a determined maximum value L of the cell or sub-region sizemaxJudging whether the convergence condition is satisfied
Or i is more than N, i is iteration times, N is cycle times, if the convergence condition is met, the current displacement field is output, and the image quality and the deformation gradient are realizedPerforming deformation characterization on the regulated digital image; otherwise, reassigning the grid or sub-area distribution according to the strain gradient of each point and the image quality, executing digital speckle correlation calculation, and repeatedly executing the step 4 and the step 5 until the convergence condition is met.
In this embodiment, Δ L is taken as 1 pixel, and N is taken as 5 times; at this time, the convergence condition is not reached, and the calculation is continued with L min10 pixels and LmaxThe grid is subdivided for a range of 29 pixels, the cell size of each point still satisfies the formula (1) and the formula (2), and c is maintained1＝c2Again, the digital speckle correlation calculation is performed at 0.5.
And (5) repeatedly executing the step 4 and the step 5, and terminating the calculation after 5 times of circulation. The maximum unit size values obtained in the 2 nd to 5 th circulation are respectively as follows:
the number of the pixels is set to be,
the number of the pixels is set to be,
the number of the pixels is set to be,
a pixel. The final grid distribution is as in fig. 4, where darker colors represent smaller grid sizes. It can be seen that locations with large strain gradients and good image quality have smaller sized cells.
FIG. 5A is a comparison between the mean error values calculated by the method of the present invention and the conventional method, and FIG. 5B is a comparison between the standard deviation error values calculated by the method of the present invention and the conventional method. The invention uses non-uniform grids, while the traditional method uses uniform grids, the grid size of which can be different. Through direct comparison, the accuracy and precision of the method are higher than those of the traditional method (or equal to the optimal value of the traditional method), and the beneficial effects of the method are proved.
Fig. 6 schematically shows a block diagram of an image processing apparatus according to an exemplary embodiment of the present invention, and as shown in fig. 6, the apparatus 600 may include, for example, an image providing module 610, a first processing module 620, a second processing module 630, a third processing module 640, and a digital image deformation characterization module 650.
Wherein:
an image providing module 610, configured to provide a speckle image before deformation and a speckle image after deformation;
a first processing module 620, configured to determine, for the speckle image before deformation and/or the speckle image after deformation, a minimum value L of a cell or sub-region size through a change of a Hessian matrix condition numbermin；
A second processing module 630 for minimizing L according to the cell or sub-region sizeminRoughly calculating a displacement field by searching or specifying initial grid or subregion distribution by integral pixels;
a third processing module 640 for obtaining a correlation function C (r) ═ t(s) t (s + r) according to the displacement field and the tangent vectormin＝C0Determining the maximum value L of the cell or sub-region sizemaxWhere t(s) is the tangent to the curve or plane at s, C0Is a critical value; and
a digital image deformation characterization module 650 for determining the maximum value L of the cell or sub-region sizemaxJudging whether the convergence condition is satisfied
Or i is more than N, i is iteration times, N is cycle times, if the convergence condition is met, the current displacement field is output, and the digital image deformation characterization of image quality and deformation gradient regulation is realized.
The device of the embodiment can be installed in a computer for digital speckle correlation calculation in a software mode to provide real-time detection; and the device can also be installed in a background server for digital speckle correlation calculation, and provides large-batch background detection.
It should be noted that the embodiment of the apparatus portion is similar to the embodiment of the method portion, and please refer to the method embodiment portion for details, which are not described herein again.
Any of the modules, units, or at least part of the functionality of any of them according to embodiments of the present invention may be implemented in one module. Any one or more of the modules and units according to the embodiments of the present invention may be implemented by being divided into a plurality of modules. Any one or more of the modules, units according to the embodiments of the present invention may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by any other reasonable means of hardware or firmware by integrating or packaging the circuits, or may be implemented by any one of three implementations of software, hardware and firmware, or any suitable combination of any of them. Alternatively, one or more of the modules, units according to embodiments of the present invention may be implemented at least partly as computer program modules, which, when executed, may perform the respective functions.
For example, any plurality of the image providing module 610, the first processing module 620, the second processing module 630, the third processing module 640, and the digital image deformation characterizing module 650 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present invention, at least one of the image providing module 610, the first processing module 620, the second processing module 630, the third processing module 640, and the digital image deformation characterization module 650 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any of them. Or at least one of the image providing module 610, the first processing module 620, the second processing module 630, the third processing module 640 and the digital image distortion characterizing module 650 may be at least partially implemented as a computer program module, which, when executed, may perform a corresponding function.
FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment. The electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, electronic device 700 includes a processor 710, a computer-readable storage medium 720. The electronic device 700 may perform a method according to an embodiment of the invention.
In particular, processor 710 may comprise, for example, a general purpose microprocessor, an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 710 may also include on-board memory for caching purposes. Processor 710 may be a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present invention.
Computer-readable storage medium 720, for example, may be a non-volatile computer-readable storage medium, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); memory such as Random Access Memory (RAM) or flash memory, etc.
The computer-readable storage medium 720 may include a computer program 721, which computer program 721 may include code/computer-executable instructions that, when executed by the processor 710, enable the processor 710 to solve this embodiment or any other variation in accordance with the methods of the present invention.
The computer program 721 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 721 may include one or more program modules, including 721A, modules 721B, … …, for example. It should be noted that the division and number of the modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, so that the processor 710 may execute the method according to the embodiment of the present invention or any other modifications when the program modules are executed by the processor 710.
At least one of the image providing module 610, the first processing module 620, the second processing module 630, the third processing module 640, and the digital image distortion characterization module 650 according to embodiments of the present invention may be implemented as a computer program module as described with reference to fig. 7, which when executed by the processor 710 may implement the corresponding operations described above.
The present invention also provides a computer-readable storage medium, which may be included in the apparatus/device/system described in the above embodiments, or may exist separately without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the present invention.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that while the present invention has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents. Accordingly, the scope of the present invention should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (10)
1. A digital image deformation characterization method for image quality and deformation gradient regulation is characterized by comprising the following steps:
step 1: providing a speckle image before deformation and a speckle image after deformation;
step 2: determining the minimum value L of the size of a unit or a subarea according to the change of the condition number of a Hessian (Hessian) matrix aiming at the speckle image before deformation and/or the speckle image after deformationmin；
And step 3: according to the minimum value L of the cell or sub-region sizeminRoughly calculating a displacement field by searching or specifying initial grid or subregion distribution by integral pixels;
and 4, step 4: according to the displacement field and the tangent vector correlation function C (r) ═ t(s) t (s + r)min＝C0Determining the maximum value L of the cell or sub-region sizemaxWhere t(s) is the tangent to the curve or plane at s, C0Is a critical value;
and 5: according to a determined maximum value L of the cell or sub-region sizemaxJudging whether the convergence condition is satisfied
Or i is more than N, i is iteration times, N is cycle times, if the convergence condition is met, the current displacement field is output, and the digital image deformation characterization of image quality and deformation gradient regulation is realized.
2. The method for characterizing digital image deformation according to claim 1, wherein the speckle image after deformation in step 1 is obtained by shifting the speckle image before deformation by a certain displacement, and a corresponding displacement field can be obtained by comparing the difference between the speckle image after deformation and the speckle image before deformation.
3. The method for characterizing digital image deformation according to claim 1, wherein the minimum value L of the unit or sub-region size is determined in step 2min，
According to the image quality of the speckle image, the speckle image before deformation is adopted, and the size of a unit or a sub-area which enables the Hessian condition number to be increased sharply is used as the minimum value of the size of the unit or the sub-area through the change of the Hessian matrix condition number; or
According to the image quality of the speckle image, the deformed speckle image is adopted, and the size of a unit or a sub-area which enables the Hessian condition number to be increased sharply is used as the minimum value of the size of the unit or the sub-area through the change of the Hessian matrix condition number; or
According to the image quality of the speckle images, the speckle images before deformation and the speckle images after deformation are respectively adopted, the minimum value of the size of a unit or a sub-area is respectively determined, and then the average value is taken as the minimum value of the size of the unit or the sub-area.
4. The method for characterizing digital image deformation through image quality and deformation gradient regulation according to claim 1, wherein the unit or sub-area in step 2 is a triangle or a quadrangle.
5. The method for characterizing digital image deformation according to claim 1, wherein the speckle images before deformation and the speckle images after deformation are used in calculating the displacement field in step 3, and the speckle images before deformation and the speckle images after deformation correspond to one displacement field.
6. The method for characterizing digital image deformation according to claim 1, wherein the step 3 of calculating the displacement field uses a global digital speckle correlation method or a local digital speckle correlation method.
7. The method for characterizing digital image deformation through image quality and deformation gradient regulation according to claim 1, wherein the step 5 further comprises:
if the convergence condition is not met, reassigning the grid or sub-area distribution according to the strain gradient and the image quality of each point, executing digital speckle correlation calculation, and returning to execute the step 4 and the step 5 until the convergence condition is met.
8. A digital image deformation characterization device for image quality and deformation gradient regulation is characterized by comprising:
the image providing module is used for providing a speckle image before deformation and a speckle image after deformation;
a first processing module, which is used for determining the minimum value L of the unit or sub-area size according to the change of the condition number of the Hessian matrix aiming at the speckle image before deformation and/or the speckle image after deformationmin；
A second processing module for minimizing L according to the cell or sub-region sizeminRoughly calculating a displacement field by searching or specifying initial grid or subregion distribution by integral pixels;
a third processing module for obtaining a correlation function C (r) ═ t(s) t (s + r) according to the displacement field and the tangent vectormin＝C0Determining the maximum value L of the cell or sub-region sizemaxWhere t(s) is the tangent to the curve or plane at s, C0Is a critical value; and
a digital image deformation characterization module for determining the maximum value L of the cell or sub-region sizemaxJudging whether the convergence condition is satisfied
Or i > N, i being the number of iterations, N beingAnd (4) circulating times, if the convergence condition is met, outputting the current displacement field, and realizing digital image deformation characterization of image quality and deformation gradient regulation.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the digital image deformation characterization method of any one of claims 1-7.
10. A computer-readable storage medium storing computer-executable instructions for implementing the digital image deformation characterization method of any one of claims 1 to 7 when executed.
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