Thermal effect correction method and system based on Beta spline surface fitting

Abstract

The present invention discloses a thermal effect correction method and system based on Beta spline surface fitting, the method comprises: obtaining a thermal effect filter image of a thermal effect degraded image; sampling to obtain a discretized image, converting each point therein into a type value point of a cubic Beta spline surface, and optimizing the Beta spline surface problem as a spline curve problem; according to the deviation parameter and tangent parameter of the step spline curve within a preset range of the coefficient of determination, solving the optimal parameter group of all spline curves in the direction and direction; weightedly calculating the optimal deviation parameter and optimal tangent parameter of the surface according to the optimal parameter group of all spline curves, inversely calculating the corresponding surface control point matrix, solving the cubic Beta spline surface, and then gray-stretching the surface to obtain a thermal effect intensity field; iteratively optimizing the thermal effect degraded image to obtain a corrected image. The present invention can more effectively remove the thermal effect of optical imaging of supersonic aircraft targets.

Description

Technical Field

[0001] The invention relates to the field of surface fitting and aerodynamic optical thermal effect correction, and in particular to a thermal effect correction method and system based on Beta spline surface fitting. Background Art

[0002] The complex physical process of the interaction of heat, light and force in the optical imaging window of supersonic aircraft has a great impact on the target optical imaging under hypersonic conditions. The high temperature radiation of the optical window and flow field caused by aerodynamic heating causes the target infrared imaging saturation and target submersion, which is called thermal effect. The thermal effect makes it difficult for the target imaging system under hypersonic conditions to perform infrared imaging of the target in front, which has become an obstacle to the infrared imaging navigation and positioning of supersonic aircraft and a technical bottleneck for the development of high-end technology of hypersonic aircraft.

[0003] Most existing methods are based on solving multi-order polynomial surface coefficients to fit the thermal effect intensity field. Since high-order polynomials are complex to calculate and difficult to solve, lower-order (second-order or third-order) multi-segment surfaces are often used for fitting. This may cause the final fitted overall surface to be discontinuous in pieces, or continuous but not smooth (not differentiable at the connection endpoints), making it difficult to ensure the accurate expression of the "overall smoothness and piecewise continuity" characteristics of the thermal effect field. Summary of the invention

[0004] The main purpose of the present invention is to provide a thermal effect correction method and system based on Beta spline surface fitting that can effectively remove thermal effects.

[0005] The technical solution adopted by the present invention is:

[0006] A thermal effect correction method based on Beta spline surface fitting is provided, comprising the following steps:

[0007] S1, obtaining a thermal effect degraded image and filtering it to filter out effective information of the image, and obtaining a thermal effect filtered image that retains low-frequency information of thermal radiation noise;

[0008] S2, sampling the thermal effect filtered image to obtain a discretized image;

[0009] S3, converting each point in the discretized image into a type value point of a cubic Beta spline surface, and optimizing the Beta spline surface problem is to solve the spline curve problem;

[0010] S4. According to The type points of a spline curve in the direction and the preset initial deviation parameters of the basis function and tangent parameter Inversely calculate the control point matrix of the spline curve and calculate the coefficient of determination R² of the spline curve at this time; step the deviation parameter within the preset range and tangent parameter , calculate the corresponding control point matrix, and calculate the determination coefficient R² of the spline curve at each step, where the maximum determination coefficient corresponds to the optimal deviation parameter and optimal tangent parameter of the spline curve; solve it in the same way Direction and The optimal parameter set of all spline curves in the direction;

[0011] S5. Perform weighted summation based on the optimal parameter groups of all spline curves to obtain the optimal deviation parameter and the optimal tangent parameter of the spline surface, inversely calculate the corresponding Beta spline surface control point matrix, solve the cubic Beta spline surface corresponding to the discretized image, and then perform grayscale stretching on the surface to obtain the thermal effect intensity field;

[0012] S6. Iteratively optimize the thermal effect degraded image according to the thermal effect intensity field to obtain a corrected image.

[0013] Following the above technical solution, in step S5, the maximum coefficient of determination is used as the weight of the corresponding optimal parameter to obtain the weighted average optimal fitting coefficient of the surface, and the basis function matrix of the surface is constructed based on the optimal fitting coefficient of the surface, and the corresponding Beta spline surface control point matrix is ​​inversely calculated to solve the cubic Beta spline surface.

[0014] Following the above technical solution, in step S6, an optimization correction method based on double prior regularization is specifically used to process the thermal effect degraded image to obtain a corrected image.

[0015] Following the above technical solution, the deviation parameter The value range is (0, 5], the tangent parameter The value range is [0, 5], and the step is 0.1.

[0016] Following the above technical solution, step S2 specifically performs sliding downsampling on the thermal effect filter image to obtain a downsampled discretized image.

[0017] Following the above technical solution, the coefficient of determination R² is:

[0018]

[0019] in is the total sum of squares, is the residual sum of squares.

[0020] The present invention also provides a thermal effect correction system based on Beta spline surface fitting, comprising:

[0021] A thermal effect filter image acquisition module is used to acquire and filter a thermal effect degraded image, filter out effective information of the image, and obtain a thermal effect filter image that retains low-frequency information of thermal radiation noise;

[0022] A discrete module is used to sample the thermal effect filtered image to obtain a discretized image;

[0023] The cubic Beta spline surface solving module is used to convert each point in the discretized image into a cubic Beta spline surface point, and the optimization of the Beta spline surface problem is to solve the spline curve problem; according to The type points of a spline curve in the direction and the preset initial deviation parameters of the basis function and tangent parameter Inversely calculate the control point matrix of the spline curve and calculate the coefficient of determination R² of the spline curve at this time; step the deviation parameter within the preset range and tangent parameter , calculate the corresponding control point matrix, and calculate the determination coefficient R² of the spline curve at each step, where the maximum determination coefficient corresponds to the optimal deviation parameter and optimal tangent parameter of the spline curve; solve it in the same way Direction and The optimal parameter group of all spline curves in the direction; the optimal deviation parameter and the optimal tangent parameter of the spline surface are obtained by weighted summation according to the optimal parameter group of all spline curves, the corresponding Beta spline surface control point matrix is ​​inversely calculated, the cubic Beta spline surface corresponding to the discretized image is solved, and then the surface is grayscale stretched to obtain the thermal effect intensity field;

[0024] The correction module is used to iteratively optimize the thermal effect degraded image according to the thermal effect intensity field to obtain a corrected image.

[0025] Following the above technical solution, the cubic Beta spline surface solving module specifically uses the maximum coefficient of determination as the weight of the corresponding optimal parameter, obtains the surface optimal fitting coefficient after weighted average, constructs the surface basis function matrix based on the surface optimal fitting coefficient, inversely calculates the corresponding Beta spline surface control point matrix, and then solves the cubic Beta spline surface.

[0026] Following the above technical solution, the correction module specifically uses an optimization correction method based on double prior regularization to process the thermal effect degraded image to obtain a corrected image.

[0027] The present invention also provides a computer storage medium, which stores a computer program that can be executed by a processor, and the computer program executes the thermal effect correction method based on Beta spline surface fitting described in the above technical solution.

[0028] The beneficial effects of the present invention are as follows: the present invention provides a thermal effect correction method based on Beta spline surface fitting, which can optimize the Beta spline surface problem to solve the spline curve problem, calculate the optimal parameters of each curve by alternating iterative optimization, obtain the optimal parameter group of the spline surface by weighted summation of the optimal parameter groups of all spline curves, inversely calculate the corresponding Beta spline surface control point matrix, and then fit the corresponding cubic Beta spline surface to obtain the thermal effect intensity field, and finally iteratively optimize the thermal effect degradation image according to the thermal effect intensity field to obtain the corrected image; the present invention breaks through the limitations of polynomial fitting, solves the continuity problem of segmented surfaces, and the Beta spline-specific deviation parameter and tangent parameter The curve shape can be controlled, it has stronger modeling ability than the B-spline curve, and can remove thermal effects more effectively.

[0029] Furthermore, the maximum coefficient of determination is used as the weight of the corresponding optimal parameter to obtain the optimal fitting coefficient of the surface after weighted average. Then, the basis function matrix of the surface is constructed based on the optimal fitting coefficient of the surface, and the corresponding Beta spline surface control point matrix is ​​inversely calculated. The optimal coefficient is selected for different images. Compared with Bezier surface and B-spline surface, this method can maintain the fitting curve and surface. Continuity, which allows local control of the shape of the curve, can more effectively remove thermal effects.

[0030] Furthermore, an optimization correction method based on double prior regularization is used to process thermal effect degraded images. The algorithm is fast and can effectively correct infrared degraded images that produce thermal effects.

[0031] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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.

[0033] Figure 1A is a flow chart of a thermal effect correction method based on Beta spline surface fitting according to an embodiment of the present invention;

[0034] Figure 1B is a flow chart of a thermal effect correction method based on Beta spline surface fitting according to another embodiment of the present invention;

[0035] Figure 2 is a schematic diagram of an original thermal effect degradation image according to an embodiment of the present invention;

[0036] Figure 3 is a schematic diagram of a filtered image after L0 filtering is performed on a thermal effect degraded image according to an embodiment of the present invention;

[0037] Figure 4 is an image schematic diagram of performing Beta surface fitting on a filter graph according to an embodiment of the present invention;

[0038] Figure 5 (a)~(j) are schematic diagrams of thermal effect correction images during the iterative process;

[0039] Figure 6 This is a schematic diagram of the thermal effect correction image generated at the end of the iteration;

[0040] Figure 7 It is a schematic diagram of the original clear image. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] It should be noted that the illustrations provided in the embodiments of the present invention are only used to illustrate the basic concept of the present invention in a schematic manner. Therefore, the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout type may also be more complicated.

[0043] In the present invention, it is also necessary to explain that, if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. appear, the orientation or position relationship indicated is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply 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 a limitation on the present application. In addition, if the terms "first" and "second" appear, they are only used for description and distinction purposes, and cannot be understood as indicating or implying relative importance.

[0044] like Figure 1A As shown, the thermal effect correction method based on Beta spline surface fitting in an embodiment of the present invention includes the following steps:

[0045] S1, obtaining a thermal effect degraded image and filtering it to filter out effective information of the image, and obtaining a thermal effect filtered image that retains low-frequency information of thermal radiation noise;

[0046] S2, sampling the thermal effect filtered image to obtain a discretized image;

[0047] S3, converting each point in the discretized image into a type value point of a cubic Beta spline surface, and optimizing the Beta spline surface problem is to solve the spline curve problem;

[0048] S4. According to The type points of a spline curve in the direction and the preset initial deviation parameters of the basis function and tangent parameter Inversely calculate the control point matrix of the spline curve and calculate the coefficient of determination R² of the spline curve at this time; step the deviation parameter within the preset range and tangent parameter , calculate the corresponding control point matrix, and calculate the coefficient of determination R² of the spline curve at each step, where the maximum coefficient of determination corresponds to the optimal deviation parameter of the spline curve and the optimal tangent parameter ; Solve the same problem Direction and The optimal parameter set of all spline curves in the direction;

[0049] S5, performing weighted summation according to the optimal parameter groups of all spline curves to obtain the optimal deviation parameters and optimal tangent parameters of the spline surface, inversely calculating the corresponding Beta spline surface control point matrix, solving the cubic Beta spline surface corresponding to the discretized image, and then gray-stretching the surface to obtain the thermal effect intensity field;

[0050] S6. Iteratively optimize the thermal effect degraded image according to the thermal effect intensity field to obtain a corrected image.

[0051] Among them, preferably, in step S5, the maximum coefficient of determination can be used as the weight of the corresponding optimal parameter to obtain the optimal fitting coefficient of the surface after weighted averaging, and the basis function matrix of the surface is constructed based on the optimal fitting coefficient of the surface, and the corresponding Beta spline surface control point matrix is ​​inversely calculated to solve the cubic Beta spline surface.

[0052] Furthermore, in step S6, an optimization correction method based on double prior regularization is specifically used to process the thermal effect degraded image to obtain a corrected image.

[0053] Among them, the deviation parameter The value range of can be (0, 5], the tangent parameter The value range of is [0, 5], and the step is 0.1. It can be understood that the value range and the step value can be adjusted accordingly according to the specific situation.

[0054] Furthermore, step S2 specifically performs sliding downsampling on the thermal effect filtered image to obtain a downsampled discretized image.

[0055] Among them, the coefficient of determination R² is:

[0056]

[0057] in is the total sum of squares, is the residual sum of squares.

[0058] The curve fitted by Beta is second-order continuous and differentiable. The maximum R² (an evaluation index for linear model evaluation) can find the Beta curve and optimal coefficient that are most similar to the discrete values ​​of the original image, which means that this curve can best explain the discrete points on this line. The coefficients of the surface are obtained by combining the coefficients of all curves, which will not cause the curve processing surface problem to be split.

[0059] After the thermal effect infrared target image is projected and mapped to the image space, it presents a discrete grayscale distribution. According to the surface theory of graphics, and the continuity and smoothness of thermal effect, the thermal effect intensity distribution is described by using surface fitting, and the thermal effect data can be converted from discrete values ​​in the image space to a surface mathematical model in the geometric space. The present invention adopts cubic Beta spline surface fitting to obtain a surface that is continuous and differentiable everywhere, that is, the discrete values ​​of the image are converted into a surface. The generated Beta surface is second-order continuous and differentiable, and a better bias field can be obtained to solve the thermal effect problem; the thermal effect image data is then used to directly invert the thermal effect surface model, thereby realizing the mathematical optimization correction of the complex thermal effects of hypersonic flight.

[0060] In another embodiment of the present invention, Figure 1B As shown, the thermal effect correction method based on Beta spline surface fitting mainly includes the following steps:

[0061] S1. Obtain thermal effect degradation image ,See Figure 2 , for the degenerate graph Norm filtering to obtain the filtered thermal effect estimation image ,See Figure 3 , The size is .

[0062] S2. Thermal effect image obtained in S1 Perform sliding downsampling. First determine the sliding window , the size is , the center pixel of the sliding window is . Sliding Window , take the pixel value at the center of each sliding window as the pixel value of the sliding block, and get the image after downsampling , The size is In this example, the initialized window size is 16×16.

[0063] A point set patch with a certain continuity surrounded by a curved boundary on a rectangular domain can be expressed as a single-valued function with two parameters: ,in The three-dimensional surface of the image P is represented as a parametric surface patch. Any point on the surface patch can be represented by To represent. Image It can be expressed as .

[0064] S3. Determine the cubic Beta spline surface, then the Beta surface equation is:

[0065]

[0066] in, represents the Beta spline basis function, is the unknown control point matrix, Indicates that the points to be fitted are parameterized in two directions.

[0067] The above formula can be rewritten as:

[0068]

[0069] Convert the above equation into an equation similar to the Beta spline curve:

[0070]

[0071] All the type value points of the Beta spline curve are Control curve replaced by:

[0072]

[0073] Beta spline surfaces can be divided into Solve equations of curves in two directions.

[0074] Each cubic Beta spline curve is given by Control Points , m represents the number of model value points. Each curve contains m-1 segments, each segment is affected by 4 control points. The endpoint interpolation requires that k+1 (k is the number) multi-node endpoints should be fixed, that is, k+1 more than the control points. The corresponding node vector is .exist The repetition degree of the nodes at the beginning and end of the direction is set to 4, that is,

[0075]

[0076] in . v The node vector corresponding to the direction is

[0077]

[0078] in .

[0079] S4, cubic Beta spline basis function is as follows:

[0080]

[0081]

[0082] and are named deviation and tangent parameters, satisfying , which can be used to control curves and surfaces.

[0083] Known Type Value Point , find the control point matrix of the corresponding Beta spline surface .

[0084] First, for Direction Individual value points , calculated in reverse Control Points . Three times each The spline curve contains m-1 segments, each segment is affected by 4 control points, and its corresponding node vectors and free end boundary conditions are used as additional equations. The inverse control point equations are as follows:

[0085]

[0086] The above linear equations can be rewritten into the following matrix form:

[0087]

[0088] Among them, the elements in the coefficient matrix are all the values ​​of the Beta spline basis function and are only related to the node values.

[0089] Write the above formula as:

[0090]

[0091] Basis Functions The initial coefficients in Set to 0.1, Set to 0, then , is known, by solving the above equation by the least square method, we can inversely calculate the current and The control point matrix of the corresponding curve , thus obtaining the Beta spline curve under the current conditions.

[0092] The coefficient of determination R² was used as an indicator of the degree of fit.

[0093]

[0094] is the total sum of squares, is the residual sum of squares. The larger the R² value, the better the fitting effect, and the optimal value of R² is 1.

[0095] In the range of (0,5], the deviation parameters are stepped by 0.1. and tangent parameter , inversely calculate the corresponding control point matrix and obtain the corresponding Beta curve, calculate the coefficient of determination R² of the spline curve under each step, and then continue to step the deviation parameter and tangent parameter , until the R² value reaches the maximum value, where the maximum coefficient of determination corresponds to the optimal deviation parameter of the spline curve and the optimal tangent parameter .

[0096] This alternating optimization method is used to calculate each pair and The maximum coefficient of determination R² value obtained with the corresponding control point is determined based on the R² value m optimal coefficients of m curves and .

[0097] Similarly, yes The n optimal coefficients are obtained by alternately optimizing the n curves in the direction and .

[0098] S5. Use the R² value of each curve as a weight and m optimal coefficients and and n optimal coefficients of and Take the weighted average to get and As the optimal fitting coefficient of the surface, the basis function matrix of the surface is constructed based on this coefficient , inversely calculate the corresponding Beta spline surface control points to obtain the characteristic grid control point matrix of the surface .

[0099] The obtained basis function matrix and the control point matrix Substituting into the Beta spline surface equation of S3, we can get the Beta spline surface , and then grayscale stretch the surface, that is:

[0100]

[0101] The thermal effect intensity field is obtained, which is recorded as ,See Figure 4 .

[0102] S6. The thermal effect image degradation model is:

[0103]

[0104] , , and They are thermal effect degraded image, target image, thermal effect surface and random noise respectively. The task of thermal effect correction is to Eliminate , but it cannot be simply understood as Minus You can Calculated, because It cannot be estimated precisely, and Also included are The optimization correction method based on double prior regularization is adopted to describe it as a variational optimization problem in a statistical sense, from which a minimization problem of the energy function can be derived, that is,

[0105]

[0106] in is a fixed weight coefficient, represents the p-norm. and is the gradient operator, used to penalize and high frequency components and suppress random noise.

[0107] Solve by double loop fixed point iteration and minimization and , that is, decompose the above formula into the following two variational minimization sub-problems:

[0108]

[0109]

[0110] in, is the regularization coefficient.

[0111] 1) Loop based on fixed-point iteration: Based on estimate

[0112] assumed Known, ask ,in, Use the known value obtained in the previous iteration, that is, the fixed point. Solving the least squares minimization problem by the fixed-point iterative minimization method yields:

[0113]

[0114] 2) Loop 2 based on fixed-point iteration: based on estimate

[0115] assumed Known, ask ,in, Use the known value obtained in the last iteration as the fixed point. , the fixed-point iterative minimization method can be used to solve:

[0116]

[0117] The thermal effect correction image can be obtained by iteratively updating the above formula. and thermal effect surfaces . Figure 5 (a) to (j) show the updated thermal effect correction images during the iterative process, during which the thermal radiation features gradually disappear. Figure 6 The thermal effect correction image output at the end of the loop iteration is shown, which is different from the original image. Figure 7 By comparison, it can be found that this method can effectively remove the thermal effect.

[0118] The present invention also provides a thermal effect correction system based on Beta spline surface fitting, comprising:

[0119] A thermal effect filter image acquisition module is used to acquire and filter a thermal effect degraded image, filter out effective information of the image, and obtain a thermal effect filter image that retains low-frequency information of thermal radiation noise;

[0120] A discrete module is used to sample the thermal effect filtered image to obtain a discretized image;

[0121] The cubic Beta spline surface solving module is used to convert each point in the discretized image into a cubic Beta spline surface point, and the optimization of the Beta spline surface problem is to solve the spline curve problem; according to The type points of a spline curve in the direction and the preset initial deviation parameters of the basis function and tangent parameter Inversely calculate the control point matrix of the spline curve and calculate the coefficient of determination R² of the spline curve at this time; step the deviation parameter within the preset range and tangent parameter , calculate the corresponding control point matrix, and calculate the coefficient of determination R² of the spline curve at each step, where the maximum coefficient of determination corresponds to the optimal deviation parameter of the spline curve and the optimal tangent parameter ; Solve the same problem Direction and The optimal parameter group of all spline curves in the direction; the optimal deviation parameter and the optimal tangent parameter of the spline surface are obtained by weighted summation according to the optimal parameter group of all spline curves, the corresponding Beta spline surface control point matrix is ​​inversely calculated, the cubic Beta spline surface corresponding to the discretized image is solved, and then the surface is grayscale stretched to obtain the thermal effect intensity field;

[0122] The correction module is used to iteratively optimize the thermal effect degraded image according to the thermal effect intensity field to obtain a corrected image.

[0123] Following the above technical solution, the cubic Beta spline surface solving module specifically uses the maximum coefficient of determination as the weight of the corresponding optimal parameter, obtains the surface optimal fitting coefficient after weighted average, constructs the surface basis function matrix based on the surface optimal fitting coefficient, inversely calculates the corresponding Beta spline surface control point matrix, and then solves the cubic Beta spline surface.

[0124] Following the above technical solution, the correction module specifically uses an optimization correction method based on double prior regularization to process the thermal effect degraded image to obtain a corrected image.

[0125] The various modules of the system are mainly used to implement the various steps of the above method embodiment, which will not be described one by one here.

[0126] The present application also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, and the program realizes corresponding functions when executed by a processor. When the computer-readable storage medium of this embodiment is executed by a processor, the thermal effect correction method based on Beta spline surface fitting of the method embodiment is realized.

[0127] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0128] The order of execution of each step in the above embodiment does not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0129] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A thermal effect correction method based on Beta spline surface fitting, characterized in that: The following steps are involved: S1, obtaining a thermal effect degraded image and filtering it to filter out effective information of the image, and obtaining a thermal effect filtered image that retains low-frequency information of thermal radiation noise; S2, sampling the thermal effect filtered image to obtain a discretized image; S3, converting each point in the discretized image into a type value point of a cubic Beta spline surface, and optimizing the Beta spline surface problem is to solve the spline curve problem; S4. According to The type points of a spline curve in the direction and the preset initial deviation parameters of the basis function and tangent parameter Inversely calculate the control point matrix of the spline curve and calculate the coefficient of determination R² of the spline curve at this time; step the deviation parameter within the preset range and tangent parameter , calculate the corresponding control point matrix, and calculate the determination coefficient R² of the spline curve at each step, where the maximum determination coefficient corresponds to the optimal deviation parameter and optimal tangent parameter of the spline curve; solve it in the same way Direction and The optimal parameter set of all spline curves in the direction; the coefficient of determination , is the total sum of squares, is the residual sum of squares; S5. Perform weighted summation based on the optimal parameter groups of all spline curves to obtain the optimal deviation parameter and the optimal tangent parameter of the spline surface, inversely calculate the corresponding Beta spline surface control point matrix, solve the cubic Beta spline surface corresponding to the discretized image, and then perform grayscale stretching on the surface to obtain the thermal effect intensity field; S6. Iteratively optimize the thermal effect degraded image according to the thermal effect intensity field to obtain a corrected image.

2. The thermal effect correction method based on Beta spline surface fitting according to claim 1, characterized in that: In step S5, the maximum coefficient of determination is used as the weight of the corresponding optimal parameter to obtain the surface optimal fitting coefficient after weighted average, and the basis function matrix of the surface is constructed based on the surface optimal fitting coefficient. The corresponding Beta spline surface control point matrix is ​​inversely calculated to solve the cubic Beta spline surface.

3. The thermal effect correction method based on Beta spline surface fitting according to claim 1, characterized in that: In step S6, an optimization correction method based on double prior regularization is specifically used to process the thermal effect degraded image to obtain a corrected image.

4. The thermal effect correction method based on Beta spline surface fitting according to claim 1, characterized in that: Deviation parameter The value range is (0, 5], the tangent parameter The value range is [0, 5], and the step is 0.

1.

5. The thermal effect correction method based on Beta spline surface fitting according to claim 1, characterized in that: Step S2 specifically performs sliding downsampling on the thermal effect filter image to obtain a downsampled discretized image.

6. A thermal effect correction system based on Beta spline surface fitting, characterized in that: include: A thermal effect filter image acquisition module is used to acquire and filter a thermal effect degraded image, filter out effective information of the image, and obtain a thermal effect filter image that retains low-frequency information of thermal radiation noise; A discrete module is used to sample the thermal effect filtered image to obtain a discretized image; The cubic Beta spline surface solving module is used to convert each point in the discretized image into a cubic Beta spline surface point, and the optimization of the Beta spline surface problem is to solve the spline curve problem; according to The type points of a spline curve in the direction and the preset initial deviation parameters of the basis function and tangent parameter Inversely calculate the control point matrix of the spline curve and calculate the coefficient of determination R² of the spline curve at this time; step the deviation parameter within the preset range and tangent parameter , calculate the corresponding control point matrix, and calculate the determination coefficient R² of the spline curve at each step, where the maximum determination coefficient corresponds to the optimal deviation parameter and optimal tangent parameter of the spline curve; solve it in the same way Direction and The optimal parameter group of all spline curves in the direction; the optimal deviation parameter and the optimal tangent parameter of the spline surface are obtained by weighted summation according to the optimal parameter group of all spline curves, the corresponding Beta spline surface control point matrix is ​​inversely calculated, the cubic Beta spline surface corresponding to the discretized image is solved, and then the surface is grayscale stretched to obtain the thermal effect intensity field; the coefficient of determination is , is the total sum of squares, is the residual sum of squares; The correction module is used to iteratively optimize the thermal effect degraded image according to the thermal effect intensity field to obtain a corrected image.

7. The thermal effect correction system based on Beta spline surface fitting according to claim 6, characterized in that: The cubic Beta spline surface solving module specifically uses the maximum coefficient of determination as the weight of the corresponding optimal parameter to obtain the optimal fitting coefficient of the surface after weighted average. Based on the optimal fitting coefficient of the surface, the basis function matrix of the surface is constructed, and the corresponding Beta spline surface control point matrix is ​​inversely calculated to solve the cubic Beta spline surface.

8. The thermal effect correction system based on Beta spline surface fitting according to claim 6, characterized in that: The correction module specifically uses an optimization correction method based on double prior regularization to process the thermal effect degraded image to obtain a corrected image.

9. A computer storage medium, characterized in that A computer program executable by a processor is stored therein, and the computer program executes the thermal effect correction method based on Beta spline surface fitting as described in any one of claims 1 to 5.

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