A data processing method for a resistor array infrared imaging target simulation system

Abstract

The present invention discloses a data processing method for a resistor array infrared imaging target simulation system, which relates to the technical field of data processing. The data processing method for the resistor array infrared imaging target simulation system includes: obtaining a target analysis image of a target infrared object simulated by a resistor array; updating an image pyramid of the target infrared object according to the target analysis image of the target infrared object, and the updated image pyramid includes a historical analysis image and a target analysis image of the target infrared object; performing blur degree recognition on the target analysis image to obtain a first image accuracy of the target analysis image; constructing an image change sequence according to the first image accuracy of the target analysis image and the second image accuracies of the respective historical analysis images in the updated image pyramid in the image order in the updated image pyramid; and determining an image adjustment step size of the target infrared object by using the image change sequence.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a data processing method for a resistor array infrared imaging target simulation system. Background Art

[0002] A resistor array infrared imaging target simulation system is a device for simulating infrared targets. Obtaining a simulation image of a clear infrared target has an important role in the military security field and the industrial inspection field, and the focal length is an important factor affecting the quality of the simulation image of the infrared target during the imaging process.

[0003] In the existing methods, a fixed image adjustment step size is usually set, and the focal length is adjusted by the fixed image adjustment step size to determine the optimal focal length.

[0004] However, in this method, if the image adjustment step size is too large, it is easy to skip the optimal focal length and the imaging quality is poor; if the image adjustment step size is too small, the imaging efficiency is low. Summary of the Invention

[0005] An embodiment of the present invention provides a data processing method for a resistor array infrared imaging target simulation system, which can improve the imaging quality and the imaging efficiency at the same time.

[0006] On the one hand, an embodiment of the present invention provides a data processing method for a resistor array infrared imaging target simulation system, including:

[0007] Obtaining a target analysis image of a target infrared object simulated by a resistor array;

[0008] According to the target analysis image of the target infrared object, updating the image pyramid of the target infrared object. The updated image pyramid includes the historical analysis images and the target analysis image of the target infrared object, and the analysis images in the image pyramid are sorted in the order of the focal length adjustment process;

[0009] Performing blurriness recognition on the target analysis image to obtain the first image accuracy of the target analysis image;

[0010] According to the first image accuracy of the target analysis image and the second image accuracy of each historical analysis image in the updated image pyramid, constructing an image change sequence according to the image order in the updated image pyramid;

[0011] Using the image change sequence to determine the image adjustment step size of the target infrared object.

[0012] In the data processing method for the resistor array infrared imaging target simulation system provided by the embodiments of the present application, by performing blur recognition on the target analysis image, the first image accuracy of the target analysis image is obtained. Then, according to the first image accuracy of the target analysis image and the second image accuracies of the respective historical analysis images in the updated image pyramid, an image change sequence is constructed in the image order in the updated image pyramid, and the change trend of the image accuracy is characterized by the image change sequence. Finally, according to the change trend of the image accuracy reflected by the image change sequence, the image adjustment step size of the target infrared object is determined. In this way, the embodiments of the present application can adaptively adjust the image adjustment step size of the target infrared object according to the change trend of the image accuracy, can update the image adjustment step size in real time according to the change of the image accuracy, and do not need to adopt a fixed image adjustment step size, so as to improve the imaging quality and imaging efficiency at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0014] Figure 1 It is a schematic flowchart of the first data processing method for the resistor array infrared imaging target simulation system provided by an embodiment of the present invention;

[0015] Figure 2 It is a schematic flowchart of the second data processing method for the resistor array infrared imaging target simulation system provided by an embodiment of the present invention;

[0016] Figure 3 It is a schematic flowchart of the third data processing method for the resistor array infrared imaging target simulation system provided by an embodiment of the present invention;

[0017] Figure 4 It is a schematic flowchart of the fourth data processing method for the resistor array infrared imaging target simulation system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a data processing method for a resistor array infrared imaging target simulation system according to the present invention, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0020] It should be noted that in the embodiments of the present invention, some existing industry solutions such as certain software, components, models, etc. may be mentioned, and they should be regarded as exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of the present invention.

[0021] In the existing method, a fixed image adjustment step size is usually set, and the focal length is adjusted through the fixed image adjustment step size to determine the optimal focal length. However, in this method, if the image adjustment step size is too large, it is easy to skip the optimal focal length and result in poor imaging quality; if the image adjustment step size is too small, it is easy to lead to low imaging efficiency.

[0022] The purpose of the present invention is to provide a data processing method for a resistor array infrared imaging target simulation system. In the data processing method for a resistor array infrared imaging target simulation system provided by the embodiments of the present application, the blurriness of the target analysis image is recognized to obtain the first image accuracy of the target analysis image. Then, according to the first image accuracy of the target analysis image and the second image accuracies of the respective historical analysis images in the updated image pyramid, an image change sequence is constructed in the image order in the updated image pyramid, and the change trend of the image accuracy is characterized by the image change sequence. Finally, according to the change trend of the image accuracy reflected by the image change sequence, the image adjustment step size of the target infrared object is determined. In this way, the embodiments of the present application can adaptively adjust the image adjustment step size of the target infrared object according to the change trend of the image accuracy, can update the image adjustment step size in real time according to the change of the image accuracy, and do not need to use a fixed image adjustment step size, thereby being able to improve the imaging quality and imaging efficiency at the same time.

[0023] The following introduces the specific embodiments of the data processing method for a resistor array infrared imaging target simulation system provided by the embodiments of the present invention.

[0024] Figure 1A data processing method for a resistor array infrared imaging target simulation system is provided. The data processing method for the resistor array infrared imaging target simulation system can be applied to the resistor array infrared imaging target simulation system, and the data processing method for the resistor array infrared imaging target simulation system may include the following S101 to S105.

[0025] S101. Obtain a target analysis image of a target infrared object simulated by a resistor array.

[0026] In this embodiment, the resistor array includes a plurality of resistor elements. By heating the resistor elements to a specific temperature, the radiation characteristics of different objects in the infrared band can be mimicked.

[0027] The target infrared object is the target object to be simulated, and the target analysis image is a simulation image obtained by simulating the target infrared object through the resistor array.

[0028] As an example, the resistor array infrared imaging target simulation system simulates the target infrared object by heating the resistor array. Then, the target analysis image at the current focal length is obtained through an infrared device or an infrared camera.

[0029] As another example, after obtaining the target analysis image, the resistor array infrared imaging target simulation system also performs image correction, image denoising, and image enhancement operations on the target analysis image to improve the image quality of the target analysis image.

[0030] S102. Update the image pyramid of the target infrared object according to the target analysis image of the target infrared object. The updated image pyramid includes the historical analysis image and the target analysis image of the target infrared object, and the analysis images in the image pyramid are sorted in the order of the sequence of the focal length adjustment process.

[0031] In this embodiment, the image pyramid is used to characterize the relationship between the focal length of an image and the corresponding image resolution.

[0032] The image pyramid includes multiple levels, each level corresponds to an analysis image respectively, and the analysis images are sorted in the order of the sequence in the focal length adjustment process. Among them, the analysis image at the bottom layer is the originally obtained analysis image, and the analysis images of each upper level are the analysis images at different focal lengths in the focal length adjustment process.

[0033] As an example, after obtaining the target analysis image of the target infrared object, the resistor array infrared imaging target simulation system stores the target analysis image in the image pyramid of the target infrared object and updates the image pyramid so that the updated image pyramid includes each historical analysis image and the target analysis image.

[0034] S103. Perform blurriness recognition on the target analysis image to obtain the first image accuracy of the target analysis image.

[0035] In this embodiment, the greater the blurriness, the smaller the first image accuracy; the smaller the blurriness, the greater the first image accuracy.

[0036] As an example, the resistor array infrared imaging target simulation system performs edge detection on the target analysis image according to the target analysis image. Obtain the number of edge pixel points of the target analysis image and the distance between each edge, and then evaluate the first image accuracy of the target analysis image according to the number of edge pixel points and the distance between each edge.

[0037] Specifically, the first image accuracy of the target analysis image can be determined by setting the threshold level. According to the number of edge pixel points, obtain the first image level corresponding to the edge pixel points, and then obtain the first accurate value corresponding to the first image level; then, according to the distance between each edge, determine the second image level corresponding to the edge distance, and then obtain the second accurate value corresponding to the second image level. Finally, add the first accurate value and the second accurate value to obtain the first image accuracy of the target analysis image.

[0038] Among them, the fewer the detected number of edge pixel points, the smaller the corresponding first accurate value; the greater the detected distance between each edge, the smaller the corresponding second accurate value.

[0039] S104. According to the first image accuracy of the target analysis image and the second image accuracy of each historical analysis image in the updated image pyramid, construct an image change sequence in the image order in the updated image pyramid.

[0040] In this embodiment, the image change sequence includes multiple image accuracies, and the image change sequence is used to characterize the change trend of the image accuracy.

[0041] As an example, the resistor array infrared imaging target simulation system starts from the bottom layer of the image pyramid and sequentially obtains the image accuracies corresponding to the analysis images of each layer upward, so as to construct an image change sequence in order.

[0042] S105. Use the image change sequence to determine the image adjustment step size of the target infrared object.

[0043] In this embodiment, when the image change sequence characterizes a downward trend in image accuracy, it indicates that the focal length needs to be increased; when the image change sequence characterizes an upward trend in image accuracy, it indicates that the focal length needs to be decreased.

[0044] As an example, the resistor array infrared imaging target simulation system analyzes the image change sequence to obtain the change trend of image accuracy, and then determines the focal length adjustment direction according to the change trend of image accuracy. Finally, the original focal length is adjusted according to the focal length adjustment direction to obtain the adjusted focal length, and the image adjustment step size of the current target infrared object is obtained according to the adjusted focal length.

[0045] Through this embodiment, the first image accuracy of the target analysis image is obtained by performing blur recognition on the target analysis image. Then, according to the first image accuracy of the target analysis image and the second image accuracy of each historical analysis image in the updated image pyramid, an image change sequence is constructed in the order of the images in the updated image pyramid, and the change trend of image accuracy is characterized by the image change sequence. Finally, according to the change trend of image accuracy reflected by the image change sequence, the image adjustment step size of the target infrared object is determined. In this way, the embodiment of the present application can adaptively adjust the image adjustment step size of the target infrared object according to the change trend of image accuracy, can update the image adjustment step size in real time according to the change of image accuracy, and does not need to adopt a fixed image adjustment step size, so as to improve the imaging quality and imaging efficiency at the same time.

[0046] As an alternative embodiment, as Figure 2 shown, S101 may specifically include the following S201-S203:

[0047] S201, simulating the target infrared object through a resistor array;

[0048] S202, obtaining a simulation image of the target infrared object through an infrared device;

[0049] S203, performing gray-scale processing on the simulation image of the target infrared object to obtain a target analysis image of the target infrared object.

[0050] In this embodiment, the resistor array infrared imaging target simulation system heats the resistor elements in the resistor array to the temperature corresponding to the target infrared object according to the target infrared object.

[0051] Then, a simulation image of the target infrared object simulated by the resistor array is obtained through an infrared device, and finally, gray-scale processing is performed on the obtained simulation image of the target infrared object to obtain a target analysis image of the target infrared object.

[0052] Through this embodiment, a simulation image of the target infrared object is obtained by using an infrared device, and then gray-scale processing is performed on the simulation image of the target infrared object to obtain a target analysis image of the target infrared object. In this way, by performing gray-scale processing on the image, the computational amount of image processing can be significantly reduced, which helps to improve the speed of subsequent image processing.

[0053] As an alternative embodiment, as Figure 3 shown, S103 may specifically include the following S301 - S304:

[0054] S301, splitting the target analysis image into at least one target analysis image block;

[0055] S302, determining the blurriness of the target analysis image block by using the edge of the image block in the target analysis image block and the number of pixel points on the edge of the image block;

[0056] S303, determining the third image accuracy of the target analysis image block by using the blurriness of the target analysis image block and the blurriness of the first historical analysis image block corresponding to the target analysis image block in the target historical analysis image, where the target historical analysis image is the previous historical analysis image of the target analysis image;

[0057] S304, obtaining the first image accuracy of the target analysis image according to the third image accuracies of the respective target analysis image blocks.

[0058] In this embodiment, the third image accuracy is the image accuracy corresponding to the target analysis image block, and the first image accuracy is the image accuracy corresponding to the target analysis image.

[0059] The target historical analysis image is the historical analysis image of the layer corresponding to the target analysis image in the image pyramid, that is, the previous historical analysis image of the target analysis image.

[0060] As an example, the resistance array infrared imaging target simulation system splits the target analysis image into at least one target analysis image block according to a preset partitioning rule. Among them, the preset partitioning rule may be the size of the preset partitioned image block.

[0061] Then edge detection is performed on each target analysis image block. The number of edge pixel points of the target analysis image block and the distance between the respective edges of the target analysis image block are obtained, and then the blurriness of the target analysis image block is evaluated according to the number of edge pixel points and the distance between the edges.

[0062] Specifically, the blurriness of the target analysis image block can be determined by setting a threshold level. According to the number of edge pixel points, the first image block level corresponding to the edge pixel points is obtained, and then the first blur value corresponding to the first image block level is obtained; then according to the distance between the respective edges, the second image block level corresponding to the edge distance is determined, and then the second blur value corresponding to the second image block level is obtained. Finally, the first blur value and the second blur value are added to obtain the blurriness of the target analysis image block.

[0063] Then, compare the blurriness of the target analysis image block with the blurriness of the first historical analysis image block corresponding to the target analysis image block in the target historical analysis image. According to the difference between the blurriness of the target analysis image block and the blurriness of the first historical analysis image block, obtain the third image accuracy of the target analysis image block. Finally, an average value calculation can be performed on the third image accuracies of each target analysis image block to obtain the first image accuracy of the target analysis image.

[0064] In this embodiment, first split the target analysis image into at least one target analysis image block. Then, according to the image block edge in the target analysis image block and the number of pixel points of the image block edge, obtain the third image accuracy of each target analysis image block. Finally, according to the third image accuracies of each target analysis image block, obtain the first image accuracy of the target analysis image. In this way, the embodiment of the present application can improve the accuracy of the first image accuracy of the target analysis image by splitting the target analysis image into at least one target analysis image block, first solving the image accuracy of each target analysis image block, and then solving the image accuracy of the entire target analysis image.

[0065] As an alternative embodiment, S301 may specifically include:

[0066] Obtain a second historical analysis image block in the target historical analysis image, where the second historical analysis image block is any historical analysis image block in the target historical analysis image;

[0067] According to the second historical analysis image block, obtain the position area in the target analysis image corresponding to the second historical analysis image block;

[0068] Split the target analysis image into at least one target analysis image block according to the size of the position area.

[0069] In this embodiment, the resistance array infrared imaging target simulation system obtains any second historical analysis image block from the target historical analysis image, determines the area where the second historical analysis image block is located in the entire target historical analysis image according to the position coordinates of the four vertices of the second historical analysis image block. Then, through comparison, obtain the position area in the target analysis image corresponding to the area where the second historical analysis image block is located in the entire target historical analysis image. Finally, according to the size of the position area, split the target analysis image into at least one target analysis image block.

[0070] Wherein, when the target analysis image is the first analysis image in the image pyramid, the target analysis image is split into at least one target analysis image block according to a preset size.

[0071] Through this embodiment, the image block division size of the target analysis image is determined according to the second historical analysis image block in the target historical analysis image, and finally the target analysis image is divided into at least one target analysis image block according to the image block division size. In this way, by determining the image block division size of the target analysis image according to the second historical analysis image block in the target historical analysis image, the embodiments of the present application can ensure that the position regions corresponding to the analysis image blocks of each analysis image obtained finally are the same, thereby improving the calculation accuracy of the subsequent image accuracy.

[0072] As an alternative embodiment, S302 may specifically include:

[0073] Using the image block edge in the target analysis image block and the number of pixel points on the image block edge, determine the blurriness of the image block edge;

[0074] Perform a mean calculation on the blurriness of each image block edge included in the target analysis image block to obtain the blurriness of the target analysis image block.

[0075] In this embodiment, the resistance array infrared imaging target simulation system first performs edge detection on each target analysis image block to obtain each image block edge of the target analysis image block and the number of pixel points on each image block edge, and then evaluates the blurriness of the image block edge according to the number of pixel points on the image block edge.

[0076] Specifically, the blurriness of the image block edge can be determined by setting a threshold level. According to the number of pixel points on the image block edge, obtain the edge pixel point level corresponding to the image block edge, and then obtain the blurriness of the image block edge corresponding to the edge pixel point level.

[0077] Finally, perform a mean calculation on the blurriness of each image block edge included in the target analysis image block to obtain the blurriness of the target analysis image block.

[0078] Through this embodiment, according to the image block edge in the target analysis image block and the number of pixel points on the image block edge, the blurriness of each image block edge is obtained. Finally, a mean calculation is performed according to the blurriness of each image block edge to obtain the blurriness of the target analysis image block. In this way, by solving the blurriness of each image block edge in the target analysis image block and then obtaining the blurriness of the entire target analysis image block according to the blurriness of each image block edge, the embodiments of the present application can improve the calculation accuracy of the blurriness of the target analysis image block.

[0079] As an alternative embodiment, using the image block edge in the target analysis image block and the number of pixel points on the image block edge to determine the blurriness of the image block edge may specifically include:

[0080] Obtain the first image block edge in the target analysis image block and the second image block edge closest to the first image block edge, where the first image block edge is any one of the image block edges in the target analysis image block;

[0081] Add the number of first pixels of the first image block edge to the number of second pixels of the second image block edge to obtain the cumulative value of the number of pixels;

[0082] Divide the distance length between the first image block edge and the second image block edge by the cumulative value of the number of pixels to obtain the blur degree of the first image block edge.

[0083] In this embodiment, the blur degree of the first image block edge can be determined by the following formula 1:

[0084] Formula 1

[0085] In the formula, represents the blur degree of the i-th first image block edge, represents the number of first pixels of the i-th first image block edge, represents the number of second pixels of the second image block edge corresponding to the i-th first image block edge, represents the distance length between the first image block edge and the second image block edge. Among them, the first pixel is also the pixel on the first image block edge, and the second pixel is also the pixel on the second image block edge.

[0086] Among them, represents the sum of the number of first pixels and the number of second pixels. The larger the sum of the number of first pixels and the number of second pixels, the greater the importance of the first image block edge, and the smaller the corresponding blur degree. represents the distance length between the first image block edge and the second image block edge. The shorter the distance between the first image block edge and the second image block edge, the stronger the continuity between the two image block edges, the stronger the detail representation ability, and the smaller the corresponding blur degree.

[0087] The blur degree of the i-th first image block edge The larger it is, the greater the blur degree shown by the target analysis image block where it is located, that is, the greater the blur degree of the target analysis image block. Then the difference between the pixels of the detailed contour of the target analysis image block and the surrounding pixels is smaller, resulting in unclear image detail information obtained in the target analysis image block, and the lower the image accuracy of the target analysis image where the target analysis image block is located.

[0088] In this embodiment, the blurriness of the target analysis image block is determined by using the image block edge in the target analysis image block and the number of pixel points on the image block edge. Considering both the distance between the image block edges and the number of pixel points on the image block edge, the blurriness of the image block edge can be accurately evaluated, thereby improving the accuracy of calculating the blurriness of the target analysis image block.

[0089] As an alternative embodiment, S303 may specifically include:

[0090] Calculate the difference between the blurriness of the target analysis image block and the blurriness of the first historical analysis image block to obtain a blurriness change value;

[0091] Multiply the blurriness change value by the blurriness of the target analysis image block and then normalize it to obtain an image deviation value of the target analysis image block;

[0092] Obtain the third image accuracy of the target analysis image block according to the image deviation value of the target analysis image block.

[0093] In this embodiment, the first historical analysis image block is the image block corresponding to the target analysis image block in the previous historical analysis image of the target analysis image in the image pyramid.

[0094] As an example, the third image accuracy of the target analysis image block can be determined by the following formula 2:

[0095] Formula 2

[0096] In the formula, represents the third image accuracy of the target analysis image block, represents the normalization function, represents the blurriness of the target analysis image block, represents the blurriness of the first historical analysis image block.

[0097] Among them, represents the difference in blurriness between the blurriness of the target analysis image block and the first historical analysis image block. The greater the difference in blurriness, the greater the change in the blurriness degree of the target analysis image block in the image pyramid, that is, it indicates that there is a large difference between the actual image imaging focal length and the optimal imaging focal length at this time, and a larger image adjustment step is required to obtain a clear image, so it indicates that the image accuracy of this target analysis image block is smaller.

[0098] The third image accuracy of the target analysis image block The smaller it is, the less clear the image detail information obtained in the target analysis image block, and the smaller the first image accuracy of the target analysis image where the target analysis image block is located.

[0099] Through this embodiment, according to the blurriness of the target analysis image block and the blurriness of the first historical analysis image block corresponding to the target analysis image block in the target historical analysis image, the third image accuracy of the target analysis image block is determined. According to the change range of the blurriness degree, the third image accuracy of the target analysis image block can be accurately obtained, which helps to accurately obtain the first image accuracy of the target analysis image subsequently, and then accurately obtain the image adjustment step size, improving the imaging quality and imaging efficiency of the target infrared object.

[0100] As an alternative embodiment, S304 may specifically include:

[0101] According to the third image accuracy of each target analysis image block, obtain the image accuracy extreme difference of the target analysis image block and the image accuracy mean value of the target analysis image block;

[0102] Divide the image accuracy mean value by the image accuracy extreme difference to obtain the first image accuracy of the target analysis image.

[0103] In this embodiment, the first image accuracy of the target analysis image can be determined by the following formula 3:

[0104] Formula 3

[0105] Wherein, represents the first image accuracy of the target analysis image, represents the image accuracy mean value of the target analysis image block, represents the image accuracy extreme difference of the target analysis image block.

[0106] The image accuracy mean value of the target analysis image block The larger it is, the greater the average image accuracy of the target analysis image block in the target analysis image, and the greater the first image accuracy of the target analysis image will be. The image accuracy extreme difference of the target analysis image block reflects the concentration of the fluctuation of the third image accuracy of the target analysis image block. The smaller the image accuracy extreme difference, the more concentrated the data distribution, and the greater the first image accuracy of the target analysis image will be.

[0107] The first image accuracy of the target analysis image The larger it is, the higher the clarity of the target analysis image at this time, that is, the smaller the focal length adjustment, and the smaller the image adjustment step size of the target infrared object.

[0108] Through this embodiment, based on the fluctuation and average value of the third image accuracy of each target analysis image block in the target analysis image, the first image accuracy of the target analysis image can be accurately obtained. This helps to subsequently accurately obtain the image adjustment step size based on the first image accuracy of the target analysis image, thereby improving the imaging quality and efficiency of the target infrared object.

[0109] As an alternative embodiment, as Figure 4 shown, S105 may specifically include:

[0110] S401, using the image change sequence, determine the optimal learning rate of the target infrared object;

[0111] S402, according to the optimal learning rate, use the gradient descent algorithm to determine the image adjustment step size of the target infrared object.

[0112] In this embodiment, the resistive array infrared imaging target simulation system determines the change trend of the image accuracy of the analysis image according to the image change sequence. According to the change trend of the image accuracy of the analysis image, determine the optimal learning rate of the target infrared object.

[0113] Specifically, the optimal learning rate of the target infrared object can be determined by setting the threshold level. According to the image change sequence, construct a linear function corresponding to the image change sequence, then determine the slope level corresponding to the image change sequence according to the slope of the linear function, and then obtain the optimal learning rate of the target infrared object corresponding to the slope level.

[0114] Then use the optimal learning rate of the target infrared object as the learning rate in the gradient descent algorithm, and calculate using the gradient descent algorithm until the pre-designed calculation stop condition is reached, thereby obtaining the image adjustment step size of the target infrared object. Among them, the pre-designed calculation stop condition can be that the gradient is less than the preset threshold or the preset number of iterations is reached.

[0115] Through this embodiment, use the image change sequence to determine the optimal learning rate of the target infrared object. Then, according to the optimal learning rate, use the gradient descent algorithm to determine the image adjustment step size of the target infrared object. In this way, the embodiment of the present application determines the optimal learning rate through the image change sequence and uses the gradient descent algorithm to obtain the optimal image adjustment step size, thereby being able to improve the imaging quality and efficiency of the target infrared object.

[0116] As an alternative embodiment, S401 may specifically include:

[0117] Calculate the difference between the fourth image accuracy of the first historical analysis image in the image change sequence and the fifth image accuracy of the best analysis image with the maximum image accuracy in the image change sequence to obtain the image accuracy change value;

[0118] According to the image change sequence, obtain the sequence slope of the image change sequence;

[0119] Use the sequence slope, the image accuracy change value, and the ordinal number of the best analysis image in the image change sequence to determine the optimal learning rate of the target infrared object.

[0120] In this embodiment, the optimal learning rate of the target infrared object can be determined by the following formula 4:

[0121] Formula 4

[0122] In the formula, represents the optimal learning rate of the target infrared object, and Sign represents the sign function. represents the sequence slope of the image change sequence, which is obtained by linearly fitting the image accuracies in the image change sequence. represents the fourth image accuracy of the first historical analysis image in the image change sequence, represents the fifth image accuracy of the best analysis image. represents the ordinal number of the best analysis image in the image change sequence, where the order of the image change sequence is in the order from the bottom layer to the top layer of the image pyramid.

[0123] Among them, represents the product of the image accuracy change value and the ordinal number of the best analysis image in the image change sequence. The larger the image accuracy change value and the larger the ordinal number of the best analysis image in the image change sequence, the larger the image adjustment step size required to present a higher image quality, and thus the corresponding optimal learning rate is larger.

[0124] The optimal learning rate of the target infrared object The larger it is, the larger the amplitude of each update of the parameters in the gradient descent algorithm, that is, the larger the image adjustment step size.

[0125] Through this embodiment, use the image change sequence to calculate the optimal learning rate of the target infrared object. This helps to subsequently obtain the optimal image adjustment step size using the optimal learning rate, thereby improving the imaging quality and imaging efficiency of the target infrared object.

[0126] It should be clear that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, the detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present invention.

[0127] It should also be noted that in the exemplary embodiments mentioned in the present invention, some methods or systems are described based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0128] As described above, the above is only the specific implementation manner of the present invention. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A data processing method for a resistor array infrared imaging target simulation system, characterized in that The method includes: Obtaining a target analysis image of a target infrared object simulated by a resistor array; Updating an image pyramid of the target infrared object according to the target analysis image of the target infrared object. The updated image pyramid includes a historical analysis image and the target analysis image of the target infrared object, and the analysis images in the image pyramid are sorted in the order of the focal length adjustment process; Performing blurriness recognition on the target analysis image to obtain a first image accuracy of the target analysis image; Constructing an image change sequence according to the first image accuracy of the target analysis image and second image accuracies of the historical analysis images in the updated image pyramid in the image order in the updated image pyramid; Determining an image adjustment step size of the target infrared object by using the image change sequence.

2. The data processing method for a resistor array infrared imaging target simulation system according to claim 1, characterized in that, The obtaining a target analysis image of a target infrared object simulated by a resistor array includes: Simulating the target infrared object by the resistor array; Obtaining a simulation image of the target infrared object by an infrared device; Performing grayscale processing on the simulation image of the target infrared object to obtain the target analysis image of the target infrared object.

3. A data processing method for a resistor array infrared imaging target simulation system according to claim 1, characterized in that The performing blurriness recognition on the target analysis image to obtain a first image accuracy of the target analysis image includes: Splitting the target analysis image into at least one target analysis image block; Determining the blurriness of the target analysis image block by using the image block edge in the target analysis image block and the number of pixel points of the image block edge; Determining a third image accuracy of the target analysis image block by using the blurriness of the target analysis image block and the blurriness of a first historical analysis image block corresponding to the target analysis image block in a target historical analysis image, where the target historical analysis image is the previous historical analysis image of the target analysis image; Obtaining the first image accuracy of the target analysis image according to the third image accuracies of the target analysis image blocks.

4. The data processing method for a resistor array infrared imaging target simulation system according to claim 3, characterized in that, The splitting the target analysis image into at least one target analysis image block includes: Obtaining a second historical analysis image block in the target historical analysis image, where the second historical analysis image block is any historical analysis image block in the target historical analysis image; Obtaining a position area corresponding to the second historical analysis image block in the target analysis image according to the second historical analysis image block; Splitting the target analysis image into at least one target analysis image block according to the size of the position area.

5. A data processing method for a resistor array infrared imaging target simulation system according to claim 3, characterized in that, The determining the blurriness of the target analysis image block by using the image block edge in the target analysis image block and the number of pixel points of the image block edge includes: Determining the blurriness of the image block edge by using the image block edge in the target analysis image block and the number of pixel points of the image block edge; Performing mean calculation on the blurrinesses of the image block edges included in the target analysis image block to obtain the blurriness of the target analysis image block.

6. A data processing method for a resistor array infrared imaging target simulation system according to claim 5, characterized in that, Determining the blurriness of the image block edge in the target analysis image block based on the image block edge and the number of pixel points of the image block edge includes: Obtaining a first image block edge in the target analysis image block and a second image block edge closest to the first image block edge, where the first image block edge is any image block edge in the target analysis image block; Adding the number of first pixel points of the first image block edge to the number of second pixel points of the second image block edge to obtain an accumulated value of the number of pixel points; Dividing the distance length between the first image block edge and the second image block edge by the accumulated value of the number of pixel points to obtain the blurriness of the first image block edge.

7. A data processing method for a resistor array infrared imaging target simulation system according to claim 3, characterized in that, Determining the third image accuracy of the target analysis image block based on the blurriness of the target analysis image block and the blurriness of a first historical analysis image block corresponding to the target analysis image block in the target historical analysis image includes: Calculating the difference between the blurriness of the target analysis image block and the blurriness of the first historical analysis image block to obtain a blurriness change value; Multiplying the blurriness change value by the blurriness of the target analysis image block and then normalizing to obtain an image deviation value of the target analysis image block; Obtaining the third image accuracy of the target analysis image block based on the image deviation value of the target analysis image block.

8. A data processing method for a resistor array infrared imaging target simulation system according to claim 3, characterized in that Obtaining the first image accuracy of the target analysis image based on the third image accuracy of each target analysis image block includes: Obtaining the image accuracy range and the image accuracy mean value of the target analysis image block based on the third image accuracy of each target analysis image block; Dividing the image accuracy mean value by the image accuracy range to obtain the first image accuracy of the target analysis image.

9. A data processing method for a resistor array infrared imaging target simulation system according to claim 1, characterized in that, Determining the image adjustment step of the target infrared object based on the image change sequence includes: Determining the optimal learning rate of the target infrared object based on the image change sequence; Determining the image adjustment step of the target infrared object using the gradient descent algorithm according to the optimal learning rate.

10. A data processing method for a resistor array infrared imaging target simulation system according to claim 9, characterized in that, Determining the optimal learning rate of the target infrared object based on the image change sequence includes: Calculating the difference between the fourth image accuracy of the first historical analysis image in the image change sequence and the fifth image accuracy of the best analysis image with the highest image accuracy in the image change sequence to obtain an image accuracy change value; Obtaining the sequence slope of the image change sequence according to the image change sequence; Determining the optimal learning rate of the target infrared object using the sequence slope, the image accuracy change value, and the ordinal number of the best analysis image in the image change sequence.

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