A method for simulating optical imaging degradation effects of space targets

Abstract

The application discloses a space target optical imaging degradation effect simulation method and relates to the technical field of image processing. According to the structure and size of a target, a three-dimensional geometric model of the space target is established by using 3D software; according to the surface material properties of each component in the three-dimensional geometric model, a space target optical image is generated; the space target optical image is processed through Gaussian filtering to obtain a blurred and degraded image; according to the imaging overexposure area of the blurred and degraded image, the area where the halo artifact effect exists is calculated; for each pixel in the area where the halo artifact effect exists, the number of imaging overexposure area pixels with a distance less than a preset distance threshold from the pixel is calculated, and the gray value of the pixel is determined according to the number of imaging overexposure area pixels; the gray value of each pixel calculated by the halo artifact effect simulation is superimposed into the blurred and degraded image to obtain a space target optical simulation image containing a degradation effect. The method improves the precision of space target optical image simulation.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method for simulating the degradation effect of optical imaging of space targets. Background Technology

[0002] Research and testing of space target image processing and recognition technologies require the generation of a large number of simulated target images in real mission scenarios as a data foundation. Therefore, it is particularly necessary to carry out research on space target optical imaging simulation.

[0003] The challenge in accurately simulating optical images of space targets lies in calculating the imaging degradation effects under extreme space environments. Current research on optical imaging simulation of space targets mainly focuses on imaging characteristics and 3D model visualization, simulating the diffusion of space target points and intra-frame motion characteristics. However, it lacks simulation studies on the imaging characteristics and degradation effects under extreme space environments, resulting in the accuracy of space target optical image simulations failing to meet the requirements of research applications. Summary of the Invention

[0004] Therefore, it is necessary to provide a simulation method for the degradation effect of optical imaging of space targets, addressing the aforementioned technical problems. This method improves the simulation accuracy of optical images of space targets.

[0005] The following technical solution is adopted in this specification: This specification provides a method for simulating the degradation effect of optical imaging of space targets, including: Based on the target's structure and size, a three-dimensional geometric model of the spatial target is created using 3D software; Based on the surface material properties of each component in the 3D geometric model, the solar light intensity at a preset orientation and the grayscale values ​​of each component under camera observation are calculated to generate an optical image of the spatial target. Gaussian filtering is used to process the optical image of a space target to obtain a blurred and degraded image; Gaussian filtering is used to simulate the blurring and degraded image of a space target caused by atmospheric scattering and point diffusion effects. Based on the overexposed area of ​​the image after blurring and degrading, the area where the halo artifact effect exists is calculated; the overexposed area represents the image area where grayscale saturation occurs because the light energy received by the imaging detector exceeds its maximum threshold. For each pixel in the region where halo artifacts exist, calculate the number of overexposed pixels in the imaging region that are less than a preset distance threshold from that pixel, and determine the gray value of that pixel based on the number of overexposed pixels in the imaging region. The grayscale value of each pixel calculated by the halo artifact effect simulation is superimposed on the blurred and degraded image to obtain an optical simulation image of the space target containing the degraded effect.

[0006] Optionally, Gaussian filtering is implemented using a matrix; the size and parameters of the matrix are determined by the required degree of image degradation to be simulated; the value of the matrix is ​​given by calculation using a Gaussian function.

[0007] Optionally, based on the overexposed areas of the blurred and degraded image, the regions where halo artifacts exist are calculated, including: Detect the gray-saturated pixel regions in the blurred and degraded image, and use these gray-saturated regions as overexposed imaging areas; The overexposed area of ​​the image is expanded by performing a dilation operation; After removing the original overexposed area from the expanded overexposed area, a ring-shaped area around the overexposed area is obtained, which is the area where the halo artifact effect exists.

[0008] Optionally, the grayscale value of a pixel is determined based on the number of pixels in the overexposed imaging area, including: Based on the proportional relationship between the gray value of the halo artifact effect and the number of overexposed pixels that affect it, the number of pixels in the overexposed imaging area is substituted into the proportional relationship to obtain the gray value of that pixel; the proportional relationship is: in, pixels in the region where halo artifacts exist grayscale value, For pixels Distance less than preset distance threshold The number of pixels in the overexposed imaging area. This is the strength coefficient.

[0009] Optionally, based on the surface material properties of each component in the 3D geometric model, the solar intensity at a preset orientation and the grayscale values ​​of each component under camera observation are calculated, including: Based on the surface material properties of each component in the 3D geometric model, determine the bidirectional reflection distribution function of each component's surface; Under the sunlight intensity and camera observation at a preset orientation, the reflection intensity generated by each component is calculated based on the bidirectional reflection distribution function of each component's surface. The reflected light intensity of each component as observed by the camera is determined based on the reflection intensity generated by each component and the geometric line-of-sight occlusion relationship between each component. The grayscale value of each component is determined based on the reflected light brightness of each component.

[0010] This specification provides a simulation device for the degradation effect of optical imaging of space targets, including: The building module is used to create a three-dimensional geometric model of the target in space using 3D software, based on the target's structure and size. The calculation module is used to calculate the sunlight intensity at a preset orientation and the grayscale value of each component of the target under camera observation based on the surface material properties of each component in the three-dimensional geometric model, and generate an optical image of the spatial target. The first degradation module is used to process the optical image of the space target through Gaussian filtering to obtain a blurred and degraded image; the Gaussian filtering process is used to simulate the blurring and degradation effect of space target imaging caused by atmospheric scattering and point diffusion effects; The second degradation module is used to calculate the area where the halo artifact effect exists based on the overexposed area of ​​the image after blurring and degradation. The overexposed area represents the image area where grayscale saturation occurs because the light energy received by the imaging detector exceeds its maximum threshold. For each pixel in the area where the halo artifact effect exists, the number of overexposed pixels that are less than a preset distance threshold to that pixel is calculated, and the grayscale value of that pixel is determined based on the number of overexposed pixels. The module is used to superimpose the grayscale value of each pixel calculated by the halo artifact effect simulation onto the blurred and degraded image to obtain an optical simulation image of the space target containing the degraded effect.

[0011] This specification provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described simulation method for the degradation effect of optical imaging of space targets.

[0012] This specification provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described simulation method for the degradation effect of optical imaging of space targets.

[0013] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects: In the simulation method for the degradation effect of optical imaging of space targets provided in this specification, it was found through research that the degradation effect of optical imaging of space targets mainly includes blur degradation effect and halo artifact effect. This method simulates the decrease in the sharpness of the target edge caused by the blur degradation effect through Gaussian filtering, and simulates the image features similar to the actual halo artifact near the overexposed area through grayscale calculation. Therefore, the accuracy of the simulation of optical imaging of space targets can be improved by using this degradation effect simulation method. Attached Figure Description

[0014] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0015] Figure 1 This is a schematic diagram illustrating a typical degradation effect of an optical image of a space target, as provided in this specification. Figure 2 This is a schematic diagram illustrating the overall degradation effect of optical imaging of a space target, as provided in this specification. Figure 3 This document provides a schematic flowchart of a simulation method for the degradation effect of optical imaging of space targets. Figure 4 A schematic diagram illustrating the construction of a three-dimensional geometric model of a space target provided in this specification; Figure 5 This specification provides a schematic diagram illustrating the simulation effect of optical imaging of a space target under different light sources and detection directions. Figure 6 This specification provides an image blurring and degradation effect simulated using Gaussian filtering with different kernel functions. Figure 7 This is a schematic diagram illustrating the simulation generation of a halo artifact effect provided in this specification; Figure 8 This is a schematic diagram illustrating the simulation process of halo artifacts caused by overexposure of a typical spatial target image, as provided in this specification. Figure 9 This document provides a schematic diagram of a computer device for simulating the degradation effect of optical imaging of space targets, as provided in this specification. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0017] Currently, research related to space target imaging simulation includes: the advanced tracking time-domain analysis simulation software TASAT, the satellite visualization and identification marker tool SVST, the optoelectronic infrared sensor module EOIR, the high-fidelity modeling simulation tool PROXORTM, as well as the space scene image simulation software SurRender and the space-based optical image simulator SBOIS. In addition, researchers have also conducted some research on space target optical imaging simulation, achieving some results in areas such as visible light imaging characteristics, digital imaging simulation, and 3D model visualization.

[0018] However, simulation studies on the imaging characteristics and imaging degradation effects of extreme space environments are rarely conducted. There is a lack of simulation calculation methods for imaging degradation effects such as halo artifacts caused by overexposure of optical images of space targets, which makes it difficult for the accuracy of image simulation to meet the requirements of research applications.

[0019] This invention analyzes the imaging characteristics and degradation effects of extreme space environments.

[0020] Characteristics of imaging in extreme space environments: Due to the unique location and operational mode of space targets, optical imaging of space targets differs from ground environment imaging and Earth remote sensing imaging. It is an imaging process in the extreme environment of space, characterized by extremely dark imaging backgrounds, a single light source, and the influence of atmospheric effects.

[0021] (1) Extremely dark imaging background. The distance of on-orbit space targets is generally far. Within a tracking imaging arc, the field of view of the optical equipment can generally only detect one target. Therefore, the target in the imaging result is singular. The target identity can be confirmed based on the target's orbit and structural characteristics, and there is generally no confusion. In addition, when performing optical imaging on space targets, if there is strong light interference from sunlight or the Earth background in the imaging field of view, the target will be submerged in the strong light interference in the image and difficult to locate and identify. Therefore, ground-based optical imaging is generally selected during the dawn and dusk when the sky background is dark and the target is illuminated by sunlight. Space-based optical imaging selects deep space as the background to eliminate stray light interference from outside the target. Therefore, the optical image of space targets has the characteristic of extremely dark background. The gray value of pixels outside the target area is almost zero. This also leads to the image gray value distribution being concentrated in the low gray value range near zero.

[0022] (2) Single directionality of light source. Space targets in orbit are located in the space environment, and the main light sources are sunlight, moonlight, other starlight, and atmospheric light formed by the reflection of sunlight on the Earth's surface. The brightness of sunlight in near-Earth space can reach -26 Mv (magnitude), which is much greater than the intensity of other light sources illuminating space targets. Therefore, the light source for imaging space targets can be considered as a single source of sunlight. That is, the grayscale of the target in the image is caused only by the illumination of the sun, and no other stray light acts on the target. Moreover, sunlight can be considered as parallel light with a single directionality, that is, it only illuminates the target from the single direction of the sun's position. Therefore, when sunlight illuminates the target from different directions, the area and angle of the illuminated target are different, resulting in significant differences in the target intensity and shape seen in the imaging results.

[0023] (3) Affected by atmospheric channels. The imaging distance of space targets is relatively long, reaching hundreds or even thousands of kilometers. Under this condition, the influence of long-distance transmission channels of light waves from the target to the imaging equipment must be considered. For ground-based optical imaging, light waves need to pass through the atmosphere from the location of the space target to reach the ground observation equipment. The atmospheric refractive index fluctuations caused by air particle scattering and atmospheric turbulence will cause attenuation and jitter of the target light waves, which will change the target reflected light waves reaching the observation equipment, resulting in blurring and deterioration of ground-based optical images and distortion of the target. In contrast, the transmission channel of space-based optical imaging is located in outer space. In the vacuum space, it is not affected by atmospheric effects, and the light waves can better maintain the original characteristics of the target. Therefore, space-based optical images generally have higher clarity.

[0024] Degradation effect of optical imaging of space targets: Due to the extreme imaging environment in space, the degradation coupling effect of optical images of space targets is often quite severe. Targets in the images are generally not as clear and distinguishable as ground-based imaging targets, mainly in the following four aspects.

[0025] One is the target dimming effect. Target dimming is caused by factors such as target backlighting and low surface reflectivity, resulting in small pixel grayscale values ​​in the target area and low contrast between the target area and the background, making it difficult to accurately identify the target's identity and structural composition from the image.

[0026] Second, there is the blurring and degradation effect. The blurring and degradation effect mainly occurs in ground-based optical images. It is caused by the attenuation and diffusion of the target reflected light due to atmospheric scattering effect, and the jitter of the target reflected light due to atmospheric turbulence effect. The target area in the image is blurred and degraded, the target is distorted and deformed, and the edge contours between the target and the background, as well as between the target and its components, are blurred and unclear.

[0027] Third is the "extremely bright-extremely dark" effect. The "extremely bright-extremely dark" effect is due to the unidirectional nature of the solar light source. The sunlit side of the target is very bright, while the shadowed side is very dark. The grayscale values ​​are too differentiated, resulting in significant differences in brightness in the target area. This leads to a lack of grayscale levels in the image, weakens the structural features of the target, and severely interferes with identification.

[0028] Fourth is the overexposure effect. The overexposure effect occurs when the intensity of the light reflected from the target is too high, exceeding the upper limit of the exposure response range of the camera's imaging device. This results in some areas of the target in the image being too bright and saturated with gray values, accompanied by halo artifacts in the surrounding areas. Consequently, the detailed feature information of the target in the bright areas is submerged in the saturated gray values ​​and becomes difficult to identify.

[0029] like Figure 1 As shown, Figure 1 This is a schematic diagram illustrating the degradation effect of optical images of typical space targets. Figure 1 Figure (a) in the diagram is a schematic diagram of the target dimming effect. Figure 1 Figure (b) in the diagram is a schematic diagram of the fuzzy degradation effect. Figure 1 Figure (c) in the diagram is a schematic diagram of the "extremely bright - extremely dark" effect. Figure 1 Figure (d) in the diagram is a schematic diagram of the overexposure effect.

[0030] Based on this, and addressing the need for simulated images of targets in numerous real-world mission scenarios in research and testing of space target image processing and recognition technologies, as well as the shortcomings of existing methods for calculating the degradation effects of space target optical imaging, this invention proposes a simulation method for the degradation effects of space target optical imaging. Based on the analysis of the imaging characteristics and degradation effects in extreme space environments, a three-dimensional model of the space target is constructed, generating target optical images under different light source-detection direction conditions. Gaussian filtering is used to calculate and simulate the imaging blurring degradation effects caused by atmospheric scattering and point spread effects. A simulation calculation process for halo artifact effects based on the number of overexposed pixels in the neighborhood is designed, and the comprehensive degradation effect of space target optical images is analyzed. A simulation process for the degradation effects of space target optical imaging is established, which can effectively improve the accuracy of space target optical image simulation. This method can simulate and calculate the degradation effects of space target optical imaging under different conditions, providing an effective technical approach for generating high-fidelity optical image data of space targets.

[0031] This invention, through research, reveals that during optical imaging of space targets in extreme space environments, blurring and halo artifacts couple and superimpose on the target image, resulting in image degradation. Therefore, the overall degradation effect of space target optical imaging is a superposition of blurring and halo artifacts, and these two effects occur in a sequential order. Blurring is caused by atmospheric scattering and other factors, occurring before the target light waves reach the imaging device's sensor, while halo artifacts are caused by overexposure due to strong light on the optical device's sensor, occurring after blurring.

[0032] Therefore, when simulating the degradation of optical images of space targets, we should first consider simulating the blurring degradation effect, and then calculate the halo artifact effect near the overexposed area in the image.

[0033] (1) In the above formula, The original simulation image of the space target without considering the degradation effect. Let f be a function of the effect of blurring and degradation on the image. The effect of overexposure-induced halo artifacts on the image is a function, and it can be seen that the simulated image after considering overall degradation is... The result is the original image after being simulated for both blurring / degradation and halo / artifact effects. For example... Figure 2 As shown, Figure 2 A schematic diagram illustrating the overall degradation effect of optical imaging of space targets.

[0034] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0035] Figure 3 This is a schematic diagram of a simulation method for the degradation effect of optical imaging of space targets in this specification, which specifically includes the following steps: S101. Based on the structure and size of the target, a three-dimensional geometric model of the space target is established using 3D software. Based on the surface material properties of each component in the three-dimensional geometric model, the intensity of sunlight at a preset orientation and the grayscale values ​​of each component of the target under camera observation are calculated to generate an optical image of the space target.

[0036] Based on the target information inversion, the target structural composition is obtained, and the external dimensions of the target body and load components are calculated to obtain the target's geometric parameters, i.e., dimensions. According to the target's structure and dimensions, a three-dimensional geometric model of the space target is established using specialized 3D software.

[0037] The optical imaging characteristics of space targets also depend on the surface material composition of the target. Therefore, after establishing a three-dimensional geometric model of the space target, it is necessary to set the surface material properties of each component in the three-dimensional geometric model, determine parameters such as the bidirectional reflection distribution function (BRDF) of each component's material, and generate a space target scattering model to generate an optical image of the space target. Figure 4 As shown, Figure 4 A schematic diagram illustrating the construction of a three-dimensional geometric model of a spatial target. Figure 4 Figure (a) is a schematic diagram of the 3D model. Figure 4 Figure (b) in the diagram is a schematic diagram of the mesh partitioning. Figure 4 Figure (c) in the diagram is a schematic diagram of material settings.

[0038] In one embodiment, based on the surface material properties of each component in the three-dimensional geometric model, the calculation of the sunlight intensity at a preset orientation and the grayscale value of each target component under camera observation includes the following steps: S201, Based on the surface material properties of each component in the 3D geometric model, determine the bidirectional reflection distribution function of each component's surface; where the bidirectional reflection distribution function... f r Defined as the ratio of reflected radiance per unit solid angle in a specific direction to incident radiance, as shown in the following formula; (2) in, The direction of the incident light. To observe the (launch) direction, For the direction of origin Incident light irradiance, The direction of reflected light The emitted radiance.

[0039] S202, under the sunlight intensity and camera observation at a preset orientation, calculates the reflection intensity generated by each component based on the bidirectional reflection distribution function of each component surface.

[0040] Since the imaging light source for space targets is a single-direction solar light source, according to the definition of the bidirectional reflection distribution function, in a certain observation direction... Below, the intensity of reflected light generated by a specific component It can be represented as: f r · (3) in, The incident direction is Solar irradiance.

[0041] S203, based on the reflection intensity generated by each component and the geometric line-of-sight occlusion relationship between each component, determine the reflected light brightness of each component observed by the camera; based on the reflected light brightness of each component, determine the grayscale value of each component of the target.

[0042] The direction of sunlight incident on each component surface is determined based on the orientation of that surface. and the direction of reflected light And the bidirectional reflection distribution function of the surface material of the component. f r The reflection intensity generated on the surface of the component is calculated according to the above formula (3). The surface of each component observed by the camera is calculated according to the above method. The reflection intensity generated by each component surface is added together, and the geometric line-of-sight occlusion relationship between each component is taken into account to determine the reflected light brightness of each component observed by the camera.

[0043] To accurately simulate the optical imaging effects of space targets under various lighting directions and spatial viewing angles, after establishing a space target scattering model, it is necessary to set different azimuth and elevation angles for the incident sunlight, as well as the azimuth and elevation angles for camera observation, covering all possible optical imaging azimuth conditions of space targets at certain angular intervals (e.g., 10° intervals). Based on this, under each azimuth condition, the reflection intensity of each target component is calculated according to the BRDF characteristics of the material, and the geometric occlusion relationship between the target components is considered. This determines the reflected light brightness of each part of the target observed by the camera, thereby determining the grayscale values ​​of each part of the target in the simulation image and generating optical images of the space target under different light source-detection directions. For example... Figure 5 As shown, Figure 5 A schematic diagram illustrating the simulation effect of optical imaging of space targets under different light sources and detection directions.

[0044] S102, Gaussian filtering is used to process the optical image of the space target to obtain a blurred and degraded image; Gaussian filtering is used to simulate the blurring and degraded image of the space target caused by atmospheric scattering and point diffusion effects.

[0045] The reflected light from a spatial target is scattered by the atmosphere during transmission, and the point spread effect after the light wave passes through the imaging optical system changes the intensity distribution of the reflected light on the imaging detector. This causes the sharpness of the target structure edges and contours in the image to decrease and become blurred, resulting in a blurring and degradation effect. The point spread function (PSF) is used to describe the distribution of a point light source after passing through the imaging system. It is usually expressed as a two-dimensional or three-dimensional function, describing the spatial distribution of light intensity. PSF models are usually expressed using Gaussian functions, Airy disks, Zernike polynomials, Moffat functions, etc. Among them, the Gaussian function is the most commonly used form of the PSF model because it is simple to calculate and suitable for real-time processing, as shown in formula (4). It is often used to describe the blurring effect in the optical system.

[0046] (4) in, For amplitude, Let σ be the standard deviation and the center position be σ. Pixels in the image.

[0047] This invention uses Gaussian filtering to simulate the blurring and degradation effect of spatial target imaging. Gaussian filtering is implemented using a matrix (convolution kernel); the size and parameters of the matrix are determined by the degree of image degradation to be simulated. The stronger the blurring and degradation effect to be simulated, the larger the size of the Gaussian filtering matrix is ​​selected. For example, matrices with side lengths of 7, 15, and 35 are used for simulating weak, moderate, and strong blurring and degradation effects, respectively. The matrix value is calculated using a Gaussian function. First, the weight of each element in the matrix is ​​calculated. :

[0048] (5) Where (x, y) are the position coordinates of the elements in the matrix, σ is the standard deviation, and the elements of the weight matrix are then normalized so that the sum of the weights of all matrix elements is 1. This matrix is ​​used to scan each pixel in the optical image of the spatial target, and the value of the center pixel of the template is replaced by the weighted average gray value of the pixels in the neighborhood determined by the template (matrix). For example, the simplest Gaussian filter convolution kernel (matrix) with ksize=(3,3) can be in the following form:

[0049] = (6) By adjusting parameters such as the kernel function ksize (size of the filter matrix) of the Gaussian filter, the effects of varying degrees of blurring and degradation on optical images of spatial targets can be effectively simulated. For example... Figure 6 As shown, Figure 6 The image blurring and degradation effects of Gaussian filtering simulations under different kernel functions are shown in Figure (a) with kernel size ksize=(11, 11), Figure (b) with kernel size ksize=(25,25), Figure (c) with kernel size ksize=(55,55), and Figure (d) with kernel size ksize=(85,85).

[0050] S103, calculate the area where the halo artifact effect exists based on the overexposed area of ​​the image after blurring and degrading; for each pixel in the area where the halo artifact effect exists, calculate the number of overexposed pixels that are less than a preset distance threshold from that pixel, and determine the gray value of that pixel based on the number of overexposed pixels.

[0051] The overexposed area refers to the image area where the grayscale is saturated due to the light energy received by the imaging detector exceeding its maximum threshold.

[0052] A halo artifact is a transitional region that appears near high-contrast boundaries caused by overexposure in an image. What should have been a sharp edge becomes blurred, and a gradually transitioning region appears on both sides of the edge. This transitional region looks like a "halo," hence the name "halo artifact." The halo artifact not only affects the sharpness of image object edges but also leads to a loss of detail.

[0053] Halo artifacts appear near overexposed areas and have the following characteristics: the stronger the overexposure and the larger the area, the greater the distribution range and intensity of the halo artifacts; the closer to the overexposed area, the greater the intensity of the halo artifacts, and they gradually weaken with increasing distance from the overexposed area. Based on the characteristics of the halo artifact effect, this invention designs a halo artifact simulation generation method: based on the overexposed area of ​​the image after blurring and degrading, the area where the halo artifact effect exists is calculated; for each pixel in the area where the halo artifact effect exists, the number of overexposed pixels within a preset distance threshold is calculated, and the gray value of the pixel is determined based on the number of overexposed pixels.

[0054] Specifically, in one embodiment, the region where the halo artifact effect exists is calculated based on the overexposed region of the image after blurring and degrading. This includes: detecting pixel regions with gray-level saturation (gray-level value of 255) in the image after blurring and degrading, and taking these gray-level saturated regions as the overexposed regions; performing a dilation operation on the overexposed regions to expand their range; wherein, during the dilation operation, a binary matrix (such as a 3×3 matrix of all 1s) is first defined, and the anchor point (center point) of this matrix is ​​aligned with each pixel on the image (sliding pixel by pixel). As long as the matrix overlaps with the overexposed region of the image, the pixel at the anchor point position in the image is set as the foreground. Applying this rule to all pixels yields the image with the expanded overexposed region; removing the original overexposed region R0 (i.e., the gray-level saturated region in the original image) from the expanded overexposed region R1 yields the annular region R2 surrounding the overexposed region, i.e. The annular region R2 is the area where the halo artifact effect exists.

[0055] Determining the grayscale value of a pixel based on the number of pixels in the overexposed imaging area includes: substituting the number of pixels in the overexposed imaging area into the proportional relationship between the grayscale value of the halo artifact effect and the number of overexposed pixels that affect it, to obtain the grayscale value of the pixel; the proportional relationship is: (7) in, pixels in the region where halo artifacts exist grayscale value, For pixels Distance less than preset distance threshold The number of pixels in the overexposed imaging area. This is the strength coefficient.

[0056] like Figure 7 As shown, Figure 7 This is a schematic diagram of the simulation generation of halo artifact effect. Point P in the diagram is a point in the region where the halo artifact effect exists, that is, a point outside the overexposed area.

[0057] It is evident that when simulating image halo artifacts, the distance threshold can be changed. The value adjusts the range of the halo artifact area caused by overexposure. The higher the value, the larger the area affected by overexposure and the resulting halo artifacts; this can be mitigated by adjusting the intensity coefficient. The value adjusts the intensity of the halo artifact effect. The higher the value, the brighter the halo artifact area. Figure 8 This diagram illustrates the simulation process of halo artifacts caused by overexposure in a typical spatial target image. (a) shows the original image, (b) shows the overexposed area, (c) shows the overexposure-blown area, (d) shows the area where the halo artifact effect exists, and (e) shows the effect after adding the halo artifact. The distance threshold used in the simulation calculation is... Strength coefficient .

[0058] S104. The gray values ​​of each pixel calculated by the halo artifact effect simulation are superimposed on the blurred and degraded image to obtain an optical simulation image of the space target containing the degraded effect.

[0059] To verify the simulation method for the degradation effect of optical imaging of space targets proposed in this invention, three sets of simulated optical images of space targets (named Target 1, Target 2, and Target 3, respectively) under strong, medium, and weak brightness conditions were selected as objects, and simulation calculations and analyses of the degradation effect of imaging were performed under different conditions.

[0060] (1) Image of a spatial target with high brightness The image degradation results for a bright target 1 under different intensities of blurring and halo artifact effects are calculated, as shown in Table 1. The blurring degradation conditions are set to no blurring effect, weak blurring effect (ksize=(7,7), σ=10), medium blurring effect (ksize=(15,15), σ=20), and strong blurring effect (ksize=(35,35), σ=30), respectively simulating the degradation effects under four different degrees: no atmospheric scattering effect, strong atmospheric scattering effect, medium atmospheric scattering effect, and weak atmospheric scattering effect. The halo artifact degradation conditions are set to no halo artifact effect, weak halo artifact effect (ksize=(7,7), σ=10), medium blurring effect (ksize=(15,15), σ=20), and strong blurring effect (ksize=(35,35), σ=30), respectively simulating the degradation effects under four different degrees: no atmospheric scattering effect, strong atmospheric scattering effect, medium atmospheric scattering effect, and weak atmospheric scattering effect. d =15, k=0.2) and strong halo artifact effect ( d =30, k =0.15), respectively simulating the degradation effect under three different levels: no halo artifact effect, strong halo artifact effect, and weak halo artifact effect.

[0061] Table 1 As shown in Table 1, with the increase of the Gaussian filter kernel function ksize and σ value, the blurring effect of the target in the image gradually becomes more severe, the edge contour clarity of the target parts decreases, and the target structure becomes increasingly blurry and difficult to identify. This is because the larger the kernel function ksize value, the larger the area of ​​a pixel that is affected during the Gaussian filtering process, and the more pixels there are. The effect of the gray value being "smoothed" by the gray value of the surrounding area is more obvious, the gray value gradient at the target edge contour is smaller, and the gray value feature changes more slowly. Furthermore, with the increase of the halo artifact simulation parameters and distance threshold... d and strength coefficient k As the value increases, the halo artifacts around the overexposed areas of the target in the image become more severe. This is because the distance threshold is set... d The larger the value, the farther the halo artifact effect occurs at the pixels in the overexposed area, and the larger the halo artifact area formed near the overexposed area. The intensity coefficient is set accordingly. k The larger the value, the larger the gray value of the halo artifact region pixel calculated from the number of overexposed pixels in the neighborhood, and the brighter the halo artifact region appears. By superimposing the two imaging degradation effects, it is possible to accurately simulate the optical image of space targets under different degrees of blurring and halo artifact effects, and it can be used to simulate the imaging effect of space targets under different mission scenarios and environmental conditions.

[0062] (2) Spatial target image with moderate target brightness The image degradation results for target 2 with moderate brightness under different intensities of blurring and halo artifact effects are calculated, as shown in Table 2. The settings for blurring degradation and halo artifact effects are the same as those for target 1 in Table 1.

[0063] Table 2 As shown in Table 2, target 2 is similar to target 1. As the values ​​of the Gaussian filter kernel function ksize and σ increase, the blurring effect of the target in the image gradually becomes more severe, the edge contour clarity of the target parts decreases, and the target structure becomes blurred. The overexposed area of ​​the medium-brightness target in the image is small, and the halo artifact effect is relatively weak. Moreover, when the blurring effect is strong, the overexposed area is "smoothed" by the surrounding darker areas, and the brightness decreases. The bright areas of the target and the halo artifact effect no longer exist in the image. That is, the halo artifact effect will be weakened and disappear due to the enhancement of the blurring effect.

[0064] (3) Image of a spatial target with weak brightness The image of the target 3 with weak brightness was calculated and generated under different intensities of blurring effect and halo artifact effect, as shown in Table 3. The settings of blurring degrading effect and halo artifact effect are the same as those in Tables 1 and 2 above.

[0065] Table 3 As shown in Table 3, similar to the previous two target cases, as the values ​​of the Gaussian filter kernel function ksize and σ increase, the blurring effect of target 3 in the image gradually becomes more severe, the edge contour clarity of the target component decreases, and the overall brightness of the target further darkens; however, since the brightness of target 3 is relatively weak, there is no overexposure effect, so no halo artifact effect will appear in the simulation results regardless of the setting of the halo artifact effect intensity.

[0066] Simulation results of the imaging degradation effect of space targets with different brightness under various blurring effects and halo artifacts show that the space target optical imaging degradation simulation method proposed in this invention can effectively and accurately simulate and calculate the space target optical imaging degradation effect under different conditions, and can provide an effective technical approach for the generation of simulation images and the construction of datasets in real space mission scenarios.

[0067] The execution subject of the methods provided in this specification can be a server, which can be a server set up on a business platform, or a device such as a desktop computer or laptop computer that can execute the scheme in this specification.

[0068] When applying the simulation method for the degradation effect of optical imaging of space targets provided in this manual, it is not necessary to consider... Figure 3 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this manual does not impose any restrictions on it.

[0069] The above describes one or more embodiments of a space target optical imaging degradation effect simulation method provided in this specification. Based on the same idea, this specification also provides a corresponding space target optical imaging degradation effect simulation device, which includes: The building module is used to create a three-dimensional geometric model of the target in space using 3D software, based on the target's structure and size. The calculation module is used to calculate the sunlight intensity at a preset orientation and the grayscale value of each component of the target under camera observation based on the surface material properties of each component in the three-dimensional geometric model, and generate an optical image of the spatial target. The first degradation module is used to process the optical image of the space target through Gaussian filtering to obtain a blurred and degraded image; the Gaussian filtering process is used to simulate the blurring and degradation effect of space target imaging caused by atmospheric scattering and point diffusion effects; The second degradation module is used to calculate the area where the halo artifact effect exists based on the overexposed area of ​​the image after blurring and degradation. The overexposed area represents the image area where grayscale saturation occurs because the light energy received by the imaging detector exceeds its maximum threshold. For each pixel in the area where the halo artifact effect exists, the number of overexposed pixels that are less than a preset distance threshold to that pixel is calculated, and the grayscale value of that pixel is determined based on the number of overexposed pixels. The module is used to superimpose the grayscale value of each pixel calculated by the halo artifact effect simulation onto the blurred and degraded image to obtain an optical simulation image of the space target containing the degraded effect.

[0070] Specific limitations regarding the simulation device for the degradation effect of optical imaging of space targets can be found in the limitations of the simulation method for the degradation effect of optical imaging of space targets mentioned above, and will not be repeated here. Each module in the aforementioned simulation device for the degradation effect of optical imaging of space targets can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of the processor, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0071] This specification also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 3 A simulation method for the degradation effect of optical imaging of space targets is provided.

[0072] This instruction manual also provides Figure 9 The schematic diagram of the computer device shown is as follows: Figure 9 At the hardware level, the computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above-mentioned functions. Figure 3 A simulation method for the degradation effect of optical imaging of space targets is provided.

[0073] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0074] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for simulating the degradation effect of optical imaging of space targets, characterized in that, include: Based on the target's structure and size, a three-dimensional geometric model of the spatial target is created using 3D software; Based on the surface material properties of each component in the 3D geometric model, the solar light intensity at a preset orientation and the grayscale values ​​of each component under camera observation are calculated to generate an optical image of the spatial target. Gaussian filtering is used to process the optical image of a space target to obtain a blurred and degraded image; Gaussian filtering is used to simulate the blurring and degraded image of a space target caused by atmospheric scattering and point diffusion effects. Based on the overexposed area of ​​the image after blurring and degrading, the area where the halo artifact effect exists is calculated; the overexposed area represents the image area where grayscale saturation occurs because the light energy received by the imaging detector exceeds its maximum threshold. For each pixel in the region where halo artifacts exist, calculate the number of overexposed pixels in the imaging region that are less than a preset distance threshold from that pixel, and determine the gray value of that pixel based on the number of overexposed pixels in the imaging region. The grayscale value of each pixel calculated by the halo artifact effect simulation is superimposed on the blurred and degraded image to obtain an optical simulation image of the space target containing the degraded effect.

2. The method according to claim 1, characterized in that, Gaussian filtering is implemented using a matrix; the size and parameters of the matrix are determined by the required degree of image degradation in the simulation; the value of the matrix is ​​calculated using a Gaussian function.

3. The method according to claim 1, characterized in that, Based on the overexposed areas of the blurred and degraded image, calculate the regions where halo artifacts exist, including: Detect the gray-saturated pixel regions in the blurred and degraded image, and use these gray-saturated regions as overexposed imaging areas; The overexposed area of ​​the image is expanded by performing a dilation operation; After removing the original overexposed area from the expanded overexposed area, a ring-shaped area around the overexposed area is obtained, which is the area where the halo artifact effect exists.

4. The method according to claim 3, characterized in that, The grayscale value of a pixel is determined based on the number of pixels in the overexposed area of ​​the image, including: Based on the proportional relationship between the gray value of the halo artifact effect and the number of overexposed pixels that affect it, the number of pixels in the overexposed imaging area is substituted into the proportional relationship to obtain the gray value of that pixel; the proportional relationship is: in, pixels in the region where halo artifacts exist grayscale value, For pixels Distance less than preset distance threshold The number of pixels in the overexposed imaging area. This is the strength coefficient.

5. The method according to claim 1, characterized in that, Based on the surface material properties of each component in the 3D geometric model, the grayscale values ​​of each component of the target are calculated under the sunlight intensity at a preset orientation and under camera observation, including: Based on the surface material properties of each component in the 3D geometric model, determine the bidirectional reflection distribution function of each component's surface; Under the sunlight intensity and camera observation at a preset orientation, the reflection intensity generated by each component is calculated based on the bidirectional reflection distribution function of each component's surface. The reflected light intensity of each component as observed by the camera is determined based on the reflection intensity generated by each component and the geometric line-of-sight occlusion relationship between each component. The grayscale value of each component is determined based on the reflected light brightness of each component.

6. A simulation device for the degradation effect of optical imaging of space targets, characterized in that, include: The building module is used to create a three-dimensional geometric model of the target in space using 3D software, based on the target's structure and size. The calculation module is used to calculate the sunlight intensity at a preset orientation and the grayscale value of each component of the target under camera observation based on the surface material properties of each component in the three-dimensional geometric model, and generate an optical image of the spatial target. The first degradation module is used to process the optical image of the space target through Gaussian filtering to obtain a blurred and degraded image; the Gaussian filtering process is used to simulate the blurring and degradation effect of space target imaging caused by atmospheric scattering and point diffusion effects; The second degradation module is used to calculate the area where the halo artifact effect exists based on the overexposed area of ​​the image after blurring and degradation. The overexposed area represents the image area where grayscale saturation occurs because the light energy received by the imaging detector exceeds its maximum threshold. For each pixel in the area where the halo artifact effect exists, the number of overexposed pixels that are less than a preset distance threshold to that pixel is calculated, and the grayscale value of that pixel is determined based on the number of overexposed pixels. The module is used to superimpose the grayscale value of each pixel calculated by the halo artifact effect simulation onto the blurred and degraded image to obtain an optical simulation image of the space target containing the degraded effect.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 5.

8. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1 to 5.

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