Data processing method for resistor array infrared imaging target simulation system

Abstract

The invention discloses a data processing method for a resistor array infrared imaging target simulation system, and relates to the technical field of data processing. The data processing method for the resistor array infrared imaging target simulation system comprises the following steps: acquiring a target analysis image of a target infrared object simulated by a resistor array; according to the target analysis image of the target infrared object, an image pyramid of the target infrared object is updated, and the updated image pyramid comprises a historical analysis image of the target infrared object and the target analysis image; performing ambiguity identification on the target analysis image to obtain first image accuracy of the target analysis image; 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 an image sequence in the updated image pyramid; and determining the image adjustment step length 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 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 clearly imaged infrared target plays an important role in the fields of military security and industrial inspection, 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 complete the determination of 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 result in poor imaging quality; if the image adjustment step size is too small, it is easy to lead to low imaging efficiency. 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: Obtaining a target analysis image of a target infrared object simulated by a resistor array; Updating 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 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 sequence of the focal length adjustment process; Performing blur recognition on the target analysis image to obtain the 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 accuracy of each historical analysis image in the updated image pyramid in the image order in the updated image pyramid; Determining the image adjustment step size of the target infrared object by using the image change sequence.

[0007] In the data processing method for the resistor array infrared imaging target simulation system provided by the embodiments of the present application, by performing blurriness 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 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

[0008] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0009] 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; 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; 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; 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

[0010] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of a data processing method for a resistor array infrared imaging target simulation system proposed according to the present invention. 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.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0012] 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 considered exemplary, and their purpose is only to illustrate the feasibility in the implementation of the technical solution of the present invention.

[0013] 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.

[0014] 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, 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 image change sequence is used to characterize the change trend of the image accuracy. 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, so as to improve the imaging quality and imaging efficiency at the same time.

[0015] 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.

[0016] Figure 1 A data processing method for a resistor array infrared imaging target simulation system is provided. The data processing method for a resistor array infrared imaging target simulation system can be applied to a resistor array infrared imaging target simulation system, and the data processing method for a resistor array infrared imaging target simulation system may include the following S101 to S105.

[0017] S101, obtain a target analysis image of a target infrared object simulated by a resistor array.

[0018] In this embodiment, the resistance array includes a plurality of resistance elements. By heating the resistance elements to a specific temperature, the radiation characteristics of different objects in the infrared band can be mimicked.

[0019] 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 resistance array.

[0020] As an example, the resistance array infrared imaging target simulation system simulates the target infrared object by heating the resistance array. Then, the target analysis image at the current focal length is obtained through an infrared device or an infrared camera.

[0021] As another example, after obtaining the target analysis image, the resistance 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.

[0022] 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 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 sequence of the focal length adjustment process.

[0023] In this embodiment, the image pyramid is used to characterize the relationship between the focal length of the image and the corresponding image resolution.

[0024] 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 at the upper levels are the analysis images at different focal lengths in the focal length adjustment process.

[0025] As an example, after obtaining the target analysis image of the target infrared object, the resistance 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.

[0026] S103: Identify the blurriness of the target analysis image to obtain the first image accuracy of the target analysis image.

[0027] In this embodiment, the greater the blurriness, the smaller the first image accuracy; the smaller the blurriness, the greater the first image accuracy.

[0028] As an example, the resistor array infrared imaging target simulation system analyzes the target image and performs edge detection on the target analysis image. The number of edge pixel points of the target analysis image and the distances between the edges are obtained, and then, based on the number of edge pixel points and the distances between the edges, the first image accuracy of the target analysis image is evaluated.

[0029] Specifically, the first image accuracy of the target analysis image can be determined by setting a threshold level. Based on the number of edge pixel points, the first image level corresponding to the edge pixel points is obtained, and then the first accuracy value corresponding to the first image level is obtained; further, based on the distances between the edges, the second image level corresponding to the edge distance is determined, and then the second accuracy value corresponding to the second image level is obtained. Finally, the first accuracy value and the second accuracy value are added together to obtain the first image accuracy of the target analysis image.

[0030] Among them, the fewer the number of detected edge pixel points, the smaller the corresponding first accuracy value; the greater the distances between the detected edges, the smaller the corresponding second accuracy value.

[0031] S104. According to the first image accuracy of the target analysis image and the second image accuracies of the historical analysis images in the updated image pyramid, an image change sequence is constructed in the image order in the updated image pyramid.

[0032] 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.

[0033] 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.

[0034] S105. Using the image change sequence, determine the image adjustment step size of the target infrared object.

[0035] In this embodiment, when the image change sequence characterizes a downward trend in the image accuracy, it indicates that the focal length needs to be increased; when the image change sequence characterizes an upward trend in the image accuracy, it indicates that the focal length needs to be decreased.

[0036] As an example, the resistor array infrared imaging target simulation system analyzes the image change sequence to obtain the change trend of the image accuracy, and then determines the focal length adjustment direction according to the change trend of the 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.

[0037] Through this embodiment, by performing blurriness 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 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 embodiment 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 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.

[0038] As an alternative embodiment, as Figure 2 shown, S101 may specifically include the following S201 - S203: S201, simulating the target infrared object through a resistor array; S202, obtaining a simulation image of the target infrared object through an infrared device; S203, performing grayscale processing on the simulation image of the target infrared object to obtain the target analysis image of the target infrared object.

[0039] 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.

[0040] Then, a simulation image of the target infrared object simulated by the resistor array is obtained through an infrared device, and finally, grayscale processing is performed on the obtained simulation image of the target infrared object to obtain the target analysis image of the target infrared object.

[0041] Through this embodiment, a simulation image of the target infrared object is obtained by using an infrared device, and then grayscale processing is performed on the simulation image of the target infrared object to obtain the target analysis image of the target infrared object. In this way, by performing grayscale processing on the image, the computational amount of image processing can be significantly reduced, which helps to improve the speed of subsequent image processing.

[0042] As an alternative embodiment, as Figure 3 shown, S103 may specifically include the following S301 - S304: S301, splitting the target analysis image into at least one target analysis image block; S302, 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 on the image block edge; S303. Determine 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. The target historical analysis image is the previous historical analysis image of the target analysis image. S304. Obtain the first image accuracy of the target analysis image based on the third image accuracies of the respective target analysis image blocks.

[0043] 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.

[0044] The target historical analysis image is the historical analysis image of the layer immediately below the target analysis image in the image pyramid, that is, the previous historical analysis image of the target analysis image.

[0045] As an example, the resistor 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 can be the size of the preset partitioned image block.

[0046] Then perform edge detection on each target analysis image block. Obtain the number of edge pixel points of the target analysis image block and the distances between the respective edges of the target analysis image block, and then evaluate the blurriness of the target analysis image block based on the number of edge pixel points and the distances between the edges.

[0047] Specifically, the blurriness of the target analysis image block can be determined by setting threshold levels. Based on the number of edge pixel points, obtain the first image block level corresponding to the edge pixel points, and then obtain the first blur value corresponding to the first image block level; then, based on the distances between the respective edges, determine the second image block level corresponding to the edge distances, and then obtain the second blur value corresponding to the second image block level. Finally, add the first blur value and the second blur value to obtain the blurriness of the target analysis image block.

[0048] 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. Based on 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, the average value of the third image accuracies of the respective target analysis image blocks can be calculated to obtain the first image accuracy of the target analysis image.

[0049] Through this embodiment, the target analysis image is first split into at least one target analysis image block. Then, according to the image block edges in the target analysis image block and the number of pixel points on the image block edges, the third image accuracy of each target analysis image block is obtained. Finally, according to the third image accuracy of each target analysis image block, the first image accuracy of the target analysis image is obtained. In this way, in the embodiment of the present application, by splitting the target analysis image into at least one target analysis image block, first solving the image accuracy for each target analysis image block, and then solving the image accuracy for the entire target analysis image, the accuracy of the first image accuracy of the target analysis image can be improved.

[0050] As an alternative embodiment, S301 may specifically include: 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; 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; Split the target analysis image into at least one target analysis image block according to the size of the position area.

[0051] In this embodiment, the resistor 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, by comparison, 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 is obtained. Finally, according to the size of the position area, the target analysis image is split into at least one target analysis image block.

[0052] 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.

[0053] 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, in the embodiment of the present application, 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, it can be ensured that the position areas corresponding to the analysis image blocks of each analysis image finally obtained are the same, thereby improving the calculation accuracy of the subsequent image accuracy.

[0054] As an alternative embodiment, S302 may specifically include: Determine 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 on the image block edge; Calculate the average value of the blurriness of each image block edge included in the target analysis image block to obtain the blurriness of the target analysis image block.

[0055] 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.

[0056] 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.

[0057] Finally, calculate the average value of the blurriness of each image block edge included in the target analysis image block, and the blurriness of the target analysis image block can be obtained.

[0058] 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, calculate the average value according to the blurriness of each image block edge to obtain the blurriness of the target analysis image block. In this way, the embodiment of the present application can improve the accuracy of calculating the blurriness of the target analysis image block 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.

[0059] As an alternative embodiment, determining the blurriness of the image block edge based on the image block edge in the target analysis image block and the number of pixel points on the image block edge may specifically include: 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; Add 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 the cumulative value of the number of pixel points; Divide the distance length between the first image block edge and the second image block edge by the cumulative value of the number of pixel points to obtain the blurriness of the first image block edge.

[0060] In this embodiment, the blurriness of the first image block edge can be determined by the following formula 1: Formula 1 In the formula, represents the blur degree of the edge of the i-th first image block, represents the number of first pixel points on the edge of the i-th first image block, represents the number of second pixel points on the edge of the second image block corresponding to the edge of the i-th first image block, represents the distance length between the edge of the first image block and the edge of the second image block. Among them, the first pixel point is also the pixel point on the edge of the first image block, and the second pixel point is also the pixel point on the edge of the second image block.

[0061] Among them, represents the sum of the number of first pixel points and the number of second pixel points. The larger the sum of the number of first pixel points and the number of second pixel points, the greater the importance of the edge of the first image block, and the corresponding blur degree is smaller. represents the distance length between the edge of the first image block and the edge of the second image block. The shorter the distance between the edge of the first image block and the edge of the second image block, the stronger the continuity between the edges of the two image blocks, and thus the stronger the detail representation ability, and the corresponding blur degree is smaller.

[0062] The blur degree of the edge of the i-th first image block 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 pixel points of the detailed contour of the target analysis image block and the surrounding pixel points is smaller, resulting in unclear image detail information obtained in the target analysis image block, and the image accuracy of the target analysis image where the target analysis image block is located is smaller.

[0063] Through this embodiment, 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, the blur degree of the edge of the image block is determined. Considering from two aspects of the distance between the edges of the image blocks and the number of pixel points on the edge of the image block, the blur degree of the edge of the image block can be accurately evaluated, thereby improving the accuracy of calculating the blur degree of the target analysis image block.

[0064] As an alternative embodiment, S303 may specifically include: Calculating the difference between the blur degree of the target analysis image block and the blur degree of the first historical analysis image block to obtain a blur degree change value; Multiplying the blur degree change value by the blur degree of the target analysis image block and then normalizing it to obtain an image deviation value of the target analysis image block; Obtaining the third image accuracy of the target analysis image block according to the image deviation value of the target analysis image block.

[0065] 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.

[0066] As an example, the third image accuracy of the target analysis image block can be determined by the following formula 2: Formula 2 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.

[0067] Among them, represents the difference in blurriness between the blurriness of the target analysis image block and the first historical analysis image block. The larger 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 size is required to obtain a clear image, so it shows that the image accuracy of this target analysis image block is smaller.

[0068] 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.

[0069] 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.

[0070] As an alternative embodiment, S304 may specifically include: 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; Divide the image accuracy mean value by the image accuracy extreme difference to obtain the first image accuracy of the target analysis image.

[0071] In this embodiment, the first image accuracy of the target analysis image can be determined by the following formula 3: Formula 3 Wherein, represents the first image accuracy of the target analysis image, represents the average value of the image accuracies of the target analysis image blocks, represents the extreme difference value of the image accuracies of the target analysis image blocks.

[0072] The average value of the image accuracies of the target analysis image blocks The larger it is, the greater the average image accuracy of the target analysis image blocks in the target analysis image, and the greater the first image accuracy of the target analysis image. The extreme difference value of the image accuracies of the target analysis image blocks reflects the concentration of the third image accuracy fluctuation of the target analysis image blocks. The smaller the extreme difference value of the image accuracies, the more concentrated the data distribution, and the greater the first image accuracy of the target analysis image.

[0073] 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 of the target infrared object.

[0074] Through this embodiment, according to the floating situation and the average value of the third image accuracies of the target analysis image blocks in the target analysis image, the first image accuracy of the target analysis image can be accurately obtained. Thus, it helps to accurately obtain the image adjustment step according to the first image accuracy of the target analysis image, thereby improving the imaging quality and imaging efficiency of the target infrared object.

[0075] As an alternative embodiment, as Figure 4 shown, S105 may specifically include: S401, using the image change sequence to determine the optimal learning rate of the target infrared object; S402, according to the optimal learning rate, using the gradient descent algorithm to determine the image adjustment step of the target infrared object.

[0076] In this embodiment, the resistor array infrared imaging target simulation system determines the image accuracy change trend of the analysis image according to the image change sequence. According to the image accuracy change trend of the analysis image, the optimal learning rate of the target infrared object is determined.

[0077] Specifically, the optimal learning rate of the target infrared object can be determined by setting the threshold level. According to the image change sequence, a linear function corresponding to the image change sequence is constructed, and then according to the slope corresponding to the linear function, the slope level corresponding to the image change sequence is determined, and then the optimal learning rate of the target infrared object corresponding to the slope level is obtained.

[0078] Then, the optimal learning rate of the target infrared object is used as the learning rate in the gradient descent algorithm, and the gradient descent algorithm is used for calculation until a pre-designed calculation stop condition is reached, so as to obtain 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 a preset threshold or the preset number of iterations is reached.

[0079] In this embodiment, by using the image change sequence, the optimal learning rate of the target infrared object is determined. Then, according to the optimal learning rate, the gradient descent algorithm is used to determine the image adjustment step size of the target infrared object. In this way, in the embodiment of the present application, the optimal learning rate is determined through the image change sequence, and the optimal image adjustment step size is obtained by using the gradient descent algorithm, so that the imaging quality and imaging efficiency of the target infrared object can be improved.

[0080] As an optional embodiment, S401 may specifically include: 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; According to the image change sequence, obtain the sequence slope of the image change sequence; 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.

[0081] In this embodiment, the optimal learning rate of the target infrared object can be determined by the following formula 4: Formula 4 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.

[0082] 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 is required to present a higher image quality, and thus the corresponding optimal learning rate is larger.

[0083] Optimal learning rate of the target infrared object The larger it is, the larger the amplitude of each update of the parameter in the gradient descent algorithm, that is, the larger the image adjustment step size.

[0084] In this embodiment, the optimal learning rate of the target infrared object is calculated by using the image change sequence. This helps to obtain the optimal image adjustment step size by using the optimal learning rate subsequently, thereby improving the imaging quality and imaging efficiency of the target infrared object.

[0085] 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 descriptions of known methods are 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. 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.

[0086] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems 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.

[0087] As described above, only the specific embodiments of the present invention are provided. 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, which 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 comprises: Acquire a target analysis image of a target infrared object simulated by a resistor array; According to the target analysis image of the target infrared object, an image pyramid of the target infrared object is updated, wherein the updated image pyramid includes a historical analysis image of the target infrared object and the target analysis image, and each analysis image in the image pyramid is sorted according to the sequence of the focus adjustment process; Performing fuzziness recognition on the target analysis image to obtain a first image accuracy of the target analysis image; According to the first image accuracy of the target analysis image and the second image accuracy of each of the historical analysis images in the updated image pyramid, constructing an image change sequence according to the image sequence in the updated image pyramid; The image change sequence is used to determine the image adjustment step length of the target infrared object.

2. The data processing method for a resistor array infrared imaging target simulation system according to claim 1, characterized in that: The method of acquiring a target analysis image of a target infrared object simulated by a resistor array comprises: simulating the target infrared object by means of the resistor array; Acquire a simulated image of the target infrared object by using an infrared device; Grayscale processing is performed on the simulated image of the target infrared object to obtain a target analysis image of the target infrared object.

3. The data processing method for a resistor array infrared imaging target simulation system according to claim 1, characterized in that: The step of performing fuzziness 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; Determine the blurriness of the target analysis image block by using an image block edge in the target analysis image block and the number of pixels at 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 history analysis image block corresponding to the target analysis image block in a target history analysis image, wherein the target history analysis image is a history analysis image preceding the target analysis image; The first image accuracy of the target analysis image is obtained according to the third image accuracy of each target analysis image block.

4. The data processing method for a resistor array infrared imaging target simulation system according to claim 3, characterized in that: The step of splitting the target analysis image into at least one target analysis image block comprises: Acquire a second history analysis image block in the target history analysis image, where the second history analysis image block is any history analysis image block in the target history analysis image; According to the second history analysis image block, obtaining a position area in the target analysis image corresponding to the second history analysis image block; The target analysis image is split into at least one target analysis image block according to the size of the location area.

5. The data processing method for a resistor array infrared imaging target simulation system according to claim 3, characterized in that: 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 pixels at the image block edge includes: Determine the blurriness of the image block edge by using the image block edge and the number of pixels at the image block edge in the target analysis image block; The blurriness of the edge of each image block included in the target analysis image block is averaged to obtain the blurriness of the target analysis image block.

6. The data processing method for a resistor array infrared imaging target simulation system according to claim 5, characterized in that: The step of determining the blurriness of the image block edge by using the image block edge and the number of pixels at the image block edge in the target analysis image block comprises: Acquire a first image block edge in the target analysis image block and a second image block edge that is closest to the first image block edge, wherein the first image block edge is any image block edge in the target analysis image block; Adding a first number of pixels at the edge of the first image block and a second number of pixels at the edge of the second image block to obtain an accumulated value of the number of pixels; The blurriness of the edge of the first image block is obtained by dividing the distance between the edge of the first image block and the edge of the second image block by the accumulated value of the number of pixel points.

7. The 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 by using the blurriness of the target analysis image block and the blurriness of a first history analysis image block in the target history analysis image corresponding to the target analysis image block comprises: Calculate the difference between the blurriness of the target analysis image block and the blurriness of the first history analysis image block to obtain a blurriness change value; The blur variation value is multiplied by the blur of the target analysis image block and then normalized to obtain an image deviation value of the target analysis image block; A third image accuracy of the target analysis image block is obtained according to the image deviation value of the target analysis image block.

8. The data processing method for a resistor array infrared imaging target simulation system according to claim 3, characterized in that: The step of obtaining the first image accuracy of the target analysis image according to the third image accuracy of each target analysis image block comprises: According to the third image accuracy of each of the target analysis image blocks, obtaining an image accuracy range value of the target analysis image block and an image accuracy mean value of the target analysis image block; The image accuracy mean is divided by the image accuracy range to obtain a first image accuracy of the target analysis image.

9. The data processing method for a resistor array infrared imaging target simulation system according to claim 1, characterized in that: The step of determining the image adjustment step length of the target infrared object by using the image change sequence includes: Determining an optimal learning rate of the target infrared object using the image change sequence; According to the optimal learning rate, a gradient descent algorithm is used to determine the image adjustment step size of the target infrared object.

10. The data processing method for a resistor array infrared imaging target simulation system according to claim 9, characterized in that: The method of using the image change sequence to determine the optimal learning rate of the target infrared object includes: 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 highest image accuracy in the image change sequence to obtain an image accuracy change value; According to the image change sequence, obtaining a sequence slope of the image change sequence; An optimal learning rate for the target infrared object is determined by using the sequence slope, the image accuracy change value, and the sequence number of the best analysis image in the image change sequence.

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