Hypersonic vehicle thermal effect correction method and system based on graphics surface

Abstract

The present invention discloses a method and system for correcting thermal effects of hypersonic aircraft based on graphics surfaces, wherein the method comprises the following steps: S1, obtaining a degraded image of complex thermal effects of a hypersonic aircraft; S2, separating a thermal effect intensity field from the degraded image to obtain gray value data of the thermal effect intensity field; S3, constructing a geometric surface in computer graphics according to the gray value data of the thermal effect intensity field to characterize the thermal effect intensity field; S4, iteratively optimizing the parameters of the surface to obtain an optimized thermal effect image; S5, performing thermal effect correction on the degraded image according to the thermal effect image. The present invention can simplify the problem of correcting complex thermal effect images.

Description

Technical Field

[0001] The present invention relates to the field of aerodynamic optical thermal effect correction, and in particular to a hypersonic aircraft thermal effect correction method and system based on graphics surfaces. Background Art

[0002] When a hypersonic vehicle with an optical imaging payload flies, it interacts violently with the surrounding flow field, forming complex flow field structures such as boundary layer, shear layer, and shock wave, and causing complex thermal effects on the optical window. Thermal effects include not only thermal radiation, but also thermal diffusion, heat transfer and other factors. During imaging, thermal effects are aliased in the infrared image and coupled with the target information.

[0003] The problem of complex thermal effect image correction has become an obstacle to the high-end development of hypersonic vehicles and has seriously restricted the progress of hypersonic vehicles. Many scholars at home and abroad have conducted extensive research on the problem of thermal effect correction under high-speed flight conditions. At present, the hood optimization and flow field thermal effect suppression methods can reduce the impact of thermal effects, but cannot eliminate this impact; the thermal effect correction method based on the calculation of the thermal effect physical parameter model is not suitable for hypersonic flight conditions; the digital correction method of thermal effects based on prior knowledge is not enough to solve the problem of complex thermal effect correction. The intensity distribution of thermal effects in hypersonic flight presents a space-varying, time-varying, transient and ever-changing complex surface in space, and the thermal effect correction under hypersonic flight conditions has very demanding requirements for rapidity and real-time performance. Summary of the invention

[0004] The main purpose of the present invention is to provide a hypersonic vehicle thermal effect correction method and system based on graphics surfaces which can simplify thermal effect correction.

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

[0006] A method for correcting thermal effects of a hypersonic vehicle based on a graphics surface is provided, comprising the following steps:

[0007] S1. Obtain the degradation image of the complex thermal effect of the hypersonic vehicle and express the degradation image as The thermal radiation, thermal diffusion and heat transfer are integrated into the thermal effect intensity field , where For the original clear image, is the blur kernel, is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, is random noise;

[0008] S2. Separating the thermal effect intensity field from the degraded image , and the thermal effect intensity field is obtained Gray value data of

[0009] S3, according to the thermal effect intensity field The gray value data is used to construct the geometric surface in computer graphics to represent the intensity field of thermal effect;

[0010] S4, iteratively optimizing the parameters of the curved surface to obtain an optimized thermal effect image;

[0011] S5. Perform thermal effect correction on the degraded image according to the thermal effect image.

[0012] Following the above technical solution, step S4 specifically suppresses random noise during the iterative optimization process. For potential target images interference.

[0013] Following the above technical solution, the thermal effect is promoted by L2 norm regularization Correlation suppression of noise .

[0014] Following the above technical solution, step S2 specifically performs filtering preprocessing on the degraded image to remove high-frequency information of the degraded image, and the retained low-frequency information is the discrete data of the thermal effect grayscale bias field.

[0015] Following the above technical solution, in step S3, the discrete data of the thermal effect grayscale bias field is sampled, and the sampling points are used to characterize the thermal effect intensity field. The constructed geometric surface is then used to fit and approximate the thermal effect intensity field, and the iterative optimization solution of the thermal effect intensity field is converted into an iterative optimization solution of the geometric surface parameters.

[0016] Following the above technical solution, when performing surface fitting on the thermal effect intensity field, it is firstly adaptively segmented, and then suitable graphics surfaces are selected for fitting on different segmented regions.

[0017] The present invention also provides a hypersonic vehicle thermal effect correction system based on graphics surfaces, comprising:

[0018] The image acquisition module is used to obtain the degradation image of the complex thermal effect of the hypersonic vehicle and express the degradation image as The thermal radiation, thermal diffusion and heat transfer are integrated into the thermal effect intensity field , where For the original clear image, is the blur kernel, is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, is random noise;

[0019] Thermal effect intensity field separation module, used to separate the thermal effect intensity field from the degraded image , and the thermal effect Gray value data of

[0020] Surface fitting module, used to calculate the thermal effect The gray value data is used to construct a geometric surface in computer graphics to characterize the thermal effect intensity field; the parameters of the surface are iteratively optimized to obtain an optimized thermal effect image;

[0021] The thermal effect correction module is used to perform thermal effect correction on the degraded image according to the thermal effect image.

[0022] Following the above technical solution, the thermal effect intensity field separation module specifically performs filtering preprocessing on the degraded image to remove the high-frequency information of the degraded image, and the retained low-frequency information is the discrete data of the thermal effect grayscale bias field.

[0023] Following the above technical solution, the surface fitting module is specifically used to sample the discrete data of the thermal effect grayscale bias field. The sampling points are used to characterize the thermal effect intensity field, and then the constructed geometric surface is used to fit and approximate the thermal effect intensity field, converting the iterative optimization solution of the thermal effect intensity field into the iterative optimization solution of the geometric surface parameters.

[0024] 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 hypersonic aircraft thermal effect correction method based on graphics surfaces described in the above technical solution.

[0025] The beneficial effects of the present invention are as follows: the present invention rewrites the expression of the degraded image, integrates the thermal radiation, thermal diffusion and heat transfer therein into a thermal effect intensity field, then separates the thermal effect intensity field from the degraded image, and then characterizes the thermal effect intensity field by constructing a geometric surface, obtains an optimized thermal effect image by iterative optimization of the surface parameters, and corrects the thermal effect of the degraded image. The present invention expresses the ever-changing thermal effects with a surface, and adopts a computer graphics surface fitting method to transform the complex thermal effects of hypersonic flight from the physical space to the image space, thereby simplifying the complex thermal effect image correction problem.

[0026] Furthermore, in the process of iterative optimization of the surface parameters, the interference of random noise on the potential target image can be suppressed, because the gradient of the thermal effect intensity field has spatial correlation, that is, there is correlation between adjacent elements, while random noise does not have spatial correlation. Therefore, L2 norm regularization is used to promote correlation, which can effectively suppress noise and obtain a more accurate thermal effect surface.

[0027] Furthermore, thermal effect surface fitting is extremely sensitive to random noise. Therefore, before thermal effect surface fitting, the degraded image is filtered and preprocessed to filter out the high-frequency information in the image and retain the low-frequency information. The retained low-frequency information is the discrete data of the thermal effect grayscale bias field. By filtering out random noise, the surface fitting of the thermal effect intensity field can be better achieved.

[0028] Furthermore, since the thermal effect intensity field data is large after filtering out random noise, directly performing surface fitting on it will inevitably result in excessive calculation. By sampling (i.e., sampling) the discrete data of the thermal effect grayscale bias field, the sampling points are used to characterize the thermal effect intensity field, thereby reducing the calculation amount of thermal effect surface fitting.

[0029] 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

[0030] 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.

[0031] Figure 1 is a flow chart of a hypersonic vehicle thermal effect correction method based on a graphics surface according to an embodiment of the present invention;

[0032] Figure 2(a) is a thermal effect degradation image containing an aircraft target;

[0033] Figure 2(b) is a thermal effect degradation image containing a building target;

[0034] Figure 2(c) is the target image after thermal effect correction in Figure 2(a);

[0035] Figure 2(d) is the target image after thermal effect correction in Figure 2(b);

[0036] FIG3( a ) is a grayscale image of the thermal effect intensity field in FIG2( a );

[0037] FIG3( b ) is a grayscale image of the thermal effect intensity field in FIG2( b );

[0038] FIG3(c) is a thermal effect surface diagram of the surface fitting of FIG3(a);

[0039] FIG3( d ) is a thermal effect surface diagram of the surface fitting of FIG3( b );

[0040] Figure 4(a), Figure 4(b), Figure 4(c), and Figure 4(d) are the potential target images at the 5th, 10th, 15th, and 20th iterations of the iterative optimization process of Figure 2(a), respectively;

[0041] Fig. 5(a), Fig. 5(b), Fig. 5(c), and Fig. 5(d) are the potential target images at the 5th, 10th, 15th, and 20th times respectively during the iterative optimization process of Fig. 2(b). DETAILED DESCRIPTION

[0042] 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.

[0043] 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.

[0044] 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.

[0045] like Figure 1 As shown, the hypersonic vehicle thermal effect correction method based on the graphics surface in the embodiment of the present invention includes the following steps:

[0046] S1. Obtain the degradation image of the complex thermal effect of the hypersonic vehicle and express the degradation image as The thermal radiation, thermal diffusion and heat transfer are integrated into the thermal effect intensity field , where is the blur kernel, is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, is random noise;

[0047] S2. Separating the thermal effect intensity field from the degraded image , and the thermal effect intensity field is obtained Gray value data of

[0048] S3, according to the thermal effect intensity field The gray value data is used to construct the geometric surface in computer graphics to represent the intensity field of thermal effect;

[0049] S4, iteratively optimizing the parameters of the curved surface to obtain an optimized thermal effect image;

[0050] S5. Perform thermal effect correction on the degraded image according to the thermal effect image.

[0051] Specifically, for the acquired hypersonic flight complex thermal effect degradation images, in order to obtain the potential clear image corresponding to the degradation map, the hypersonic complex aerodynamic thermal effects are studied and the imaging model is analyzed to clarify the correction task.

[0052] Thermal effect is different from thermal radiation. The imaging model used to study thermal radiation degradation images is as follows:

[0053] (1)

[0054] In the formula, For hypersonic flight complex thermal effect degradation image, For the original clear image, is the blur kernel, is the window thermal radiation. However, thermal effects vary greatly, and the thermal effect degradation diagram includes not only thermal radiation , it also includes heat diffusion and heat transfer. The degradation is both time-varying and space-varying. It is found that in order to achieve target recognition after correction, it is necessary to solve the problems of thermal radiation, heat transfer and heat diffusion correction, and a new imaging model needs to be established.

[0055] Thermal radiation is a natural phenomenon in which an object emits electromagnetic waves, and its intensity is related to the temperature of the object; thermal diffusion specifically refers to the transfer of heat from a high-temperature area to a low-temperature area inside a substance; heat transfer describes how heat is transferred between different objects or different parts of the same object.

[0056] In a hypersonic airflow environment, heat exchange occurs between the airflow and the optical window and detector. The high-speed airflow itself may generate heat due to friction, compression, etc. This heat affects the optical window and detector through heat transfer, causing their temperature to rise. Thermal radiation, heat transfer, and heat diffusion occur simultaneously in this process and jointly affect the performance of the optical window and detector.

[0057] Due to the continuity of thermal effects in time and space, as well as the superposition relationship between them, they can be described by functions respectively. is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, where heat diffusion and heat transfer act on the window thermal radiation , combining thermal radiation, thermal diffusion and heat transfer into thermal effect intensity , which can be expressed as:

[0058] (2)

[0059] The intensity of the thermal effect is comprehensively evaluated through convolution operation. The obtained thermal effect intensity function can more comprehensively reflect the impact of high-speed airflow on the optical window and detector. The thermal effect model for image correction is further established, and the expression for hypersonic aircraft target imaging is obtained as follows:

[0060] (3)

[0061] In the formula For hypersonic flight complex thermal effect degradation image, For the original clear image, is the blur kernel, is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, where heat diffusion and heat transfer act on the window thermal radiation , is random noise.

[0062] In the formula It consists of 3 items: Represents the blurred potential target image, which can be used represents the potential target image, Thermal effect , is random noise.

[0063] According to the established imaging model, the problem to be solved is to remove random noise And from the degraded image Heat effect Strip out the potential target image = Separate out.

[0064] In order to obtain the potential target image, the imaging model established in step S1 of the present invention is , random noise needs to be removed first , and the thermal effect From the degraded image The thermal effect is obtained by peeling it off Gray value data is obtained to decouple the target image from the thermal effect data.

[0065] Thermal effect surface fitting is extremely sensitive to random noise. Random noise should be removed before thermal effect surface fitting. . Random noise and potential target images In the degraded image It is expressed as high-frequency information in the degraded image. Perform filtering preprocessing to filter out the high-frequency information in the image and retain the low-frequency information. The retained low-frequency information is the discrete data of the thermal effect grayscale bias field, which can be used express. The amount of data is too large. If the surface fitting is performed directly on it, the amount of calculation will inevitably be too large. In order to reduce the amount of calculation for thermal effect surface fitting, the discrete data of the thermal effect grayscale bias field is Extract some feature points Used to characterize the thermal effect intensity field and achieve the target image Thermal effect data decoupling. The grayscale bias data of discrete pixels loses the local correlation contained in the original spatial continuity of the thermal effect. Finally, you need to use the surface Perform a fitting approximation on it.

[0066] For the thermal effect grayscale bias field data obtained Since these thermal effect data are static, discrete digital signals, and the real thermal effect is a continuous analog signal, it is necessary to reconstruct a continuous thermal effect signal. . By analyzing the characteristics of thermal effects, it is found that the thermal effects can be fitted and approximated by constructing a computer graphics surface of the spatial distribution data of hypersonic complex thermal effects, so as to facilitate the subsequent removal of thermal effects. In some cases, the thermal effect may show a smooth change trend; in other cases, it may show a sudden or jump-like change.

[0067] The thermal effect under high-altitude conditions seems to be ever-changing, but in fact it is a regular, piecewise continuous distribution. Thermal radiation has the characteristics of "overall smoothness and piecewise continuity" in spatial distribution, while the geometric characteristics of thermal effects in physical space and image space remain unchanged, but they do not have the characteristics of overall smoothness in spatial distribution, but only have piecewise continuous distribution characteristics. Therefore, after adaptive segmentation of the thermal effect degraded image, geometric surfaces can be used to fit and approximate, and surfaces can be constructed using image pixel grayscale. The grayscale bias field is discrete and manifests as an irregular data distribution, but the thermal effect intensity surface distribution with intrinsic connections and rules can be optimized and extracted from the grayscale data.

[0068] Computer graphics has the ability to automatically model and fit any complex surface, and can provide a better fitting effect than polynomial fitting. Therefore, the surface fitting theory of graphics is used to solve the correction problem of complex thermal effects.

[0069] Grayscale bias field data for thermal effects When performing surface fitting, first Adaptive segmentation is performed, and suitable graphics surfaces are selected for fitting in different segmented areas. The types of surfaces include but are not limited to B-spline surfaces, Bezier surfaces, etc.

[0070] The specific steps of adaptive segmentation in one embodiment of the present invention are as follows: 1) select a constant C, which will be used to adjust the local threshold; 2) select the pixel in the second row and second column of the upper left corner of the image as the starting point; 3) select a window with a fixed size of 3*3 with the starting point as the center, and calculate the gray value mean of all pixels in the window; 4) add the calculated local mean to the constant C to obtain the local maximum threshold, subtract the calculated local mean from the constant C to obtain the local minimum threshold, that is, define the grayscale variation range of this area; 5) starting from the starting point, slide the window to traverse the pixels, and keep the size of the window as 3*3. If the gray value of the adjacent pixel is greater than the local minimum threshold and less than the local maximum threshold, it is added to the previous area; if the gray value of the adjacent pixel is not within the corresponding threshold range, it is marked as a new area, and the local maximum threshold and minimum threshold of the new area are calculated, and then the sliding window is traversed to the pixels; 6) after the traversal is completed, a plurality of thermal effect areas with different gray value ranges are obtained, and then surface fitting is performed on each area respectively.

[0071] For each area obtained, first select a specific surface, such as a B-spline surface, a Bezier surface, etc., determine the general expression form of the surface used, and use To represent the thermal effect The surface is:

[0072] (6)

[0073] In the formula, u and v are two directional parameters of the surface, and is the basic vector matrix, is an algebraic matrix, a vector matrix To control the mesh vertex matrix, set the thermal effect grayscale bias field data Along u and v The two parameter directions are decomposed into two sets of data points, and the matrix is ​​obtained and , which is used to describe the geometric shape of the surface. Then, through these two sets of data points, we can inversely calculate the control points and find the matrix Used to control the shape of the surface. It is generally a known algebraic matrix, determined by the type of surface.

[0074] According to formula (6), the thermal effect surface is obtained , please note that at this time Yes The value of the fitting is: from formula (3), we can know that the degraded image Including not only thermal effects , also contains random noise , if you use minus Although the obtained target image contains noise, the initial goal of the present invention is achieved.

[0075] In order to obtain a clearer potential target image , the present invention also provides another method.

[0076] Computer graphics surfaces for constructing spatial distribution data of complex hypersonic thermal effects After that, the initial goal of the present invention is achieved, and the thermal effect is initially obtained. and potential target images On the one hand, although most of the random noise can be filtered out during filtering, a small amount of residual random noise cannot be ruled out, and the fitted thermal effect surface The inaccuracy is caused by fitting a small amount of noise. minus , the target image obtained In the image, random noise is included, and the noise needs to be suppressed to obtain a cleaner potential target image. It can be understood that the thermal effect Due to the incomplete filtering, a small amount of random noise is included. , potential target image Since the direct subtraction method is not rigorous, it contains random noise. , but the noise content of the two is different, so they can be optimized by iterative optimization. and Suppress the noise in the

[0077] The degraded image in equation (3) Abbreviated as , potential target image Abbreviated as ,noise Abbreviated as The thermal radiation, thermal diffusion and heat transfer are integrated into the thermal effect intensity field , formula (3) can be expressed as

[0078]

[0079] At this time, the potential target image is obtained The essence is Therefore, it is necessary to further find a clean potential target image. The gradient is sparse, that is, most elements are close to zero, and only a few elements have non-zero values. These non-zero values ​​correspond to important features in the image, while random noise The gradient of is not sparse. L0 norm regularization is used to minimize the number of non-zero elements and promote sparsity, which is beneficial for potential target images. The gradient is regularized using the L0 norm, which can effectively suppress noise , get a clearer potential target image .

[0080] Thermal Effects Gradient has spatial correlation, that is, there is correlation between adjacent elements, while random noise There is no spatial correlation. L2 norm regularization is used to promote correlation and effectively suppress noise. , to obtain a more accurate thermal effect surface . This design is used for The energy function of the update iteration is equation (7). The problem of thermal effect correction is transformed into minimizing equation (7), and the update iteration is performed in the process. and .

[0081] (7)

[0082] In the formula, For the degraded image, is the potential target image, is a fixed weight coefficient, express -norm. and is the gradient operator, used to penalize and high frequency components and suppress random noise. , For punishment and Adaptive weight coefficient of gradient. Since the potential target image gradient and the thermal effect surface gradient have different characteristics, different penalty schemes are used for the potential target image gradient and the thermal effect surface gradient, namely: , different.

[0083] Applying formula (7) to and Perform update iterations, which can be split into the following two loops to solve:

[0084] Fixed-point iteration based loop 1: Based on estimate

[0085] In formula (8), it is assumed that Known, ask ,in, The known value obtained from the previous iteration, that is, the fixed point. For the degraded image, For a potentially clear image, is a fixed weight coefficient, is the gradient operator, For punishment Adaptive weight coefficient of the gradient. Solving the least squares minimization problem by fixed-point iterative minimization method yields:

[0086] (8)

[0087] Constructed thermal effect surface The essence is The initial value in the loop is It means that based on formula (8), the potential target image can be solved .

[0088] Loop 2 based on fixed-point iteration: based on estimate

[0089] Formula (9) assumes Known, ask ,in, The known value obtained in the previous iteration is the fixed point. is the degradation graph, For a potentially clear image, is a fixed weight coefficient, is the gradient operator, For punishment Adaptive weight coefficient of the gradient. For a given , the fixed-point iterative minimization method can be used to solve:

[0090] (9)

[0091] Based on formula (9), the updated thermal effect surface can be obtained: , and then based on formula (8), the updated potential target image can be obtained , and then based on formula (9), the updated thermal effect surface can be obtained , and repeat the cycle to update the potential target image and thermal effect surfaces , until a clear image of the potential target is obtained, realizing the mathematical optimization correction of hypersonic complex thermal effects driven by graphics surface theory.

[0092] The hypersonic vehicle thermal effect correction system based on graphics surfaces in the embodiment of the present invention is mainly used to implement the above method embodiment, and the system includes:

[0093] The image acquisition module is used to obtain the degradation image of the complex thermal effect of the hypersonic vehicle and express the degradation image as The thermal radiation, thermal diffusion and heat transfer are integrated into the thermal effect intensity field , where For the original clear image, is the blur kernel, is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, is random noise;

[0094] Thermal effect intensity field separation module, used to separate the thermal effect intensity field from the degraded image , and the thermal effect Gray value data of

[0095] Surface fitting module, used to calculate the thermal effect The gray value data is used to construct a geometric surface in computer graphics to characterize the thermal effect intensity field; the parameters of the surface are iteratively optimized to obtain an optimized thermal effect image;

[0096] The thermal effect correction module is used to perform thermal effect correction on the degraded image according to the thermal effect image.

[0097] For example, Figure 2(a) and Figure 2(b) are respectively degraded images of complex thermal effects of hypersonic aircraft acquired by the image acquisition module, and Figure 2(c) and Figure 2(d) are respectively target images of Figure 2(a) and Figure 2(b) after being processed by the thermal effect intensity field separation module, the surface fitting module, and the thermal effect correction module. At this time, the aircraft target in Figure 2(c) and the building target in Figure 2(d) are clearly visible.

[0098] Figures 3(a) and 3(b) are grayscale images corresponding to the thermal effect intensity field surface obtained after processing Figures 2(a) and 2(b) by the thermal effect intensity field separation module, surface fitting module, and thermal effect correction module. The thermal effect is shown as a large white area in the grayscale image, which is located in the center in Figure 3(a) and on the side in Figure 3(b). Figures 3(c) and 3(d) are computer graphics surface images corresponding to the thermal effect intensity field surface obtained after processing Figures 2(a) and 2(b) by the thermal effect intensity field separation module, surface fitting module, and thermal effect correction module. In the surface image, the horizontal and vertical coordinate values ​​represent the position of a pixel in the grayscale image. The larger the horizontal coordinate value of the pixel, the more to the right in the grayscale image; the larger the vertical coordinate value of the pixel, the lower in the grayscale image. The pixel grayscale value ranges from 0 to 255. The area with higher grayscale value in Figure 3(a) is located in the center, so the grayscale value in the center of Figure 3(c) is higher; the area with higher grayscale value in Figure 3(b) is located in the lower right side, so the grayscale value of the position with larger coordinate values ​​in the horizontal and vertical directions in Figure 3(d) is higher.

[0099] Figure 4(a) shows the potential target image of Figure 2(a) after the fifth iteration of iterative optimization in the thermal effect correction module after the thermal effect intensity field separation module and the surface fitting module. Due to the incompleteness of the thermal effect intensity field separation module, the thermal effect and the target image are not completely separated after the fifth iteration of the surface fitting module and the thermal effect correction module, so further iteration is required. Figure 4(b) shows the potential target image after the 10th iteration of iterative optimization in the thermal effect correction module. At this time, the thermal effect in the target image is further removed. Figure 4(c) shows the potential target image after the 15th iteration of iterative optimization. At this time, most of the thermal effect in the target image has been removed. Figure 4(d) shows the potential target image after the 20th iteration of iterative optimization. The aircraft target in the target image is clearly visible.

[0100] Figure 5(a) shows the potential target image of Figure 2(b) after the fifth iteration of iterative optimization in the thermal effect correction module after the thermal effect intensity field separation module and the surface fitting module. Due to the incompleteness of the thermal effect intensity field separation module, the thermal effect and the target image are not completely separated after the fifth iteration of the surface fitting module and the thermal effect correction module, so further iteration is performed. Figure 5(b) shows the potential target image after the 10th iteration of iterative optimization in the thermal effect correction module. At this time, the thermal effect in the target image is further removed. Figure 5(c) shows the potential target image after the 15th iteration of iterative optimization. At this time, most of the thermal effect in the target image has been removed. Figure 5(d) shows the potential target image after the 20th iteration of iterative optimization. The building target in the target image is clearly visible.

[0101] Furthermore, the thermal effect intensity field separation module specifically performs filtering preprocessing on the degraded image to remove high-frequency information of the degraded image, and the retained low-frequency information is the discrete data of the thermal effect grayscale bias field.

[0102] Furthermore, the surface fitting module is specifically used to sample the discrete data of the thermal effect grayscale bias field. The sampling points are used to characterize the thermal effect intensity field, and then the constructed geometric surface is used to fit and approximate the thermal effect intensity field, thereby converting the iterative optimization solution of the thermal effect intensity field into the iterative optimization solution of the geometric surface parameters.

[0103] Each module is mainly used to implement each step of the method embodiment, which will not be described in detail here.

[0104] 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 (e.g., 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, a hypersonic aircraft thermal effect correction method based on a graphics surface of the method embodiment is realized.

[0105] In general, the present invention mainly has the following technical advantages: by correcting the thermal effect in the image space, the spatial distribution characteristics of the thermal effect are transformed from the physical space to the image space, and the thermal effect distribution law is described by mathematics instead of using a complex physical parameter calculation model, which is groundbreaking; in addition, the present invention is simple to implement and the steps are concise, and the complex time-varying, space-varying, and transient thermal effect digital correction problems are summarized, simplified and unified into the surface optimization solution of the image grayscale value, realizing the transformation of the thermal effect from discrete data to the mathematical surface model, and solving the problem of the difficulty of correcting the complex thermal effects in the existing hypersonic flight.

[0106] 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.

[0107] 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.

[0108] 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 hypersonic vehicle thermal effect correction method based on graphics surfaces, characterized in that: The following steps are involved: S1. Obtain the degradation image of the complex thermal effect of the hypersonic vehicle and express the degradation image as The thermal radiation, thermal diffusion and heat transfer are integrated into the thermal effect intensity field , where For the original clear image, is the blur kernel, is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, is random noise; S2. Separating the thermal effect intensity field from the degraded image , and the thermal effect intensity field is obtained Gray value data of S3, according to the thermal effect intensity field The gray value data is used to construct the geometric surface in computer graphics to represent the intensity field of thermal effect; S4, iteratively optimizing the parameters of the curved surface to obtain an optimized thermal effect image; S5. Perform thermal effect correction on the degraded image according to the thermal effect image.

2. The hypersonic vehicle thermal effect correction method based on graphics surface according to claim 1, characterized in that: Step S4 specifically suppresses random noise during the iterative optimization process For potential target images interference.

3. The hypersonic vehicle thermal effect correction method based on graphics surface according to claim 1, characterized in that: Specifically, the thermal effect is promoted by L2 norm regularization Correlation suppression of noise .

4. The hypersonic vehicle thermal effect correction method based on graphics surface according to claim 1, characterized in that: Step S2 specifically performs filtering preprocessing on the degraded image to remove high-frequency information of the degraded image, and the retained low-frequency information is the discrete data of the thermal effect grayscale bias field.

5. The hypersonic vehicle thermal effect correction method based on graphics surface according to claim 4 is characterized in that: In step S3, the discrete data of the thermal effect grayscale bias field is sampled, and the sampling points are used to characterize the thermal effect intensity field. The constructed geometric surface is then used to fit and approximate the thermal effect intensity field, and the iterative optimization solution of the thermal effect intensity field is converted into the iterative optimization solution of the geometric surface parameters.

6. The hypersonic vehicle thermal effect correction method based on graphics surface according to claim 5, characterized in that: When performing surface fitting on the thermal effect intensity field, it is firstly segmented adaptively, and then suitable graphics surfaces are selected for fitting in different segmented regions.

7. A hypersonic vehicle thermal effect correction system based on graphics surfaces, characterized in that: include: The image acquisition module is used to obtain the degradation image of the complex thermal effect of the hypersonic vehicle and express the degradation image as The thermal radiation, thermal diffusion and heat transfer are integrated into the thermal effect intensity field , where For the original clear image, is the blur kernel, is the window thermal radiation, is the heat diffusion convolution function, is the heat transfer convolution function, is random noise; Thermal effect intensity field separation module, used to separate the thermal effect intensity field from the degraded image , and the thermal effect Gray value data of Surface fitting module, used to calculate the thermal effect The gray value data is used to construct a geometric surface in computer graphics to characterize the thermal effect intensity field; the parameters of the surface are iteratively optimized to obtain an optimized thermal effect image; The thermal effect correction module is used to perform thermal effect correction on the degraded image according to the thermal effect image.

8. The hypersonic vehicle thermal effect correction system based on graphics surface according to claim 7, characterized in that: The thermal effect intensity field separation module specifically performs filtering preprocessing on the degraded image to remove the high-frequency information of the degraded image, and the retained low-frequency information is the discrete data of the thermal effect grayscale bias field.

9. The hypersonic vehicle thermal effect correction system based on graphics surface according to claim 8, characterized in that: The surface fitting module is specifically used to sample the discrete data of the thermal effect grayscale bias field. The sampling points are used to characterize the thermal effect intensity field, and then the constructed geometric surface is used to fit and approximate the thermal effect intensity field, converting the iterative optimization solution of the thermal effect intensity field into the iterative optimization solution of the geometric surface parameters.

10. A computer storage medium, characterized in that: A computer program executable by a processor is stored therein, and the computer program executes the hypersonic vehicle thermal effect correction method based on a graphics surface as described in any one of claims 1 to 6.

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