A star image screening method and system for spatial telescope astronomical image evaluation

Abstract

The application provides a star image screening method and system for spatial telescope astronomical image evaluation, and relates to the technical field of optical astronomical image processing. The application obtains astronomical original image data observed by a spatial telescope and performs binaryzation processing, then performs connected domain analysis on a bright region included in the binaryzation image to obtain a connected domain list; performs two-dimensional Gaussian fitting calculation on each connected domain included in the connected domain list to obtain parameter information corresponding to each connected domain; and performs multi-round elimination screening according to the parameter information corresponding to each connected domain based on a preset screening condition to obtain a star image screening result. The application can quickly extract and screen out isolated high signal-to-noise ratio star images from a spatial telescope astronomical image, effectively eliminates cosmic rays, saturated star images, star clusters, overlapping star images and edge star images, and improves the accuracy and timeliness of the evaluation of the image quality, focusing and pointing of the astronomical image in the operation control field of the astronomical telescope.

Description

Technical Field

[0001] This invention relates to the field of optical astronomical image processing technology, and in particular to a method and system for star image selection in the evaluation of astronomical images from space telescopes. Background Technology

[0002] In the field of optical astronomical image processing in astronomy, large-scale sky surveys by space telescopes generate a massive number of astronomical images. The operation and control site needs to quickly evaluate the image quality, focus, and pointing of these images to ensure the quality of observation and adjust the observation parameters in a timely manner. As the core link in the evaluation process, the efficiency and accuracy of star image selection directly affect the reliability and timeliness of the overall evaluation results.

[0003] Currently, the industry commonly uses Gaussian template detection to detect star images. This technology requires convolution calculation of connected components, and then screening of connected components by calculating morphological features such as roundness, sharpness, and peak gray value, thereby separating out star images that meet the requirements for subsequent index calculations.

[0004] However, for space telescope astronomical images, this method of star detection and screening cannot meet the needs of rapid evaluation of image quality, focus, and pointing at the telescope operation and control site. This is mainly because the convolution calculation is time-consuming, the success rate of detecting cosmic rays based on the above-mentioned morphological features is not high, and it cannot effectively remove saturated stars and galaxies. It cannot simultaneously meet the needs of star image quality evaluation and focus and pointing evaluation based on astrometric calculations, thus affecting the timeliness and accuracy of rapid evaluation of astronomical images. Summary of the Invention

[0005] This invention provides a method and system for star image selection in space telescope astronomical images. It can quickly extract and select isolated high signal-to-noise ratio star images from space telescope astronomical images, effectively eliminating cosmic rays, saturated star images, galaxies, aliased star images, and edge star images, thereby improving the accuracy and timeliness of image quality, focus, and pointing evaluation at the telescope operation and control site.

[0006] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: Firstly, a method for star image selection in space telescope astronomical image evaluation is provided. The method includes: acquiring raw astronomical image data observed by a space telescope; performing binarization processing on the raw astronomical image data to obtain a binarized image corresponding to the raw astronomical image data, wherein the binarized image includes bright regions with pixel grayscale values ​​greater than or equal to a preset threshold and dark regions with pixel grayscale values ​​less than a preset threshold; performing connected component analysis on the bright regions included in the binarized image to obtain multiple connected components; selecting the multiple connected components based on the area and roundness of each connected component to obtain a list of connected components; and performing two-dimensional Gaussian fitting calculation on each connected component in the list of connected components to obtain parameter information corresponding to each connected component, wherein the parameter information includes star image flux peak value, background value, star image center coordinates, x-direction standard deviation, y-direction standard deviation, and center coordinate fitting error. Based on preset filtering conditions, multiple rounds of elimination filtering are performed according to the parameter information corresponding to each connected component to obtain star image filtering results, which include multiple filtered star images.

[0007] The method provided by this invention systematically achieves star image screening through a complete process of acquiring raw astronomical image data, binarization processing, connected component analysis, two-dimensional Gaussian fitting calculation, and multi-round elimination screening. It clarifies the specific composition of the bright and dark part classification features of the binarized image, the core information of the connected component list, and the parameter information. Furthermore, by performing multi-round elimination through preset screening conditions, it ensures the targetedness and effectiveness of the selected star images. This solves the problem of the lack of a unified and complete process for star image screening in the evaluation of space telescope astronomical images, providing a reliable star image basis for subsequent image quality, focus, and pointing assessments. This invention enables rapid star image screening of space telescope astronomical images, reduces computational costs, and improves adaptability to complex situations such as saturated star images, galaxies, aliased star images, and edge star images, thereby enhancing the accuracy and timeliness of astronomical image evaluation.

[0008] In one possible implementation of the first aspect, binarizing the raw astronomical image data includes: selecting a continuous region in the raw astronomical image data that accounts for a preset proportion of the total number of pixels; calculating the initial mean and initial standard deviation of the gray values ​​of all pixels in each continuous region; removing pixels that exceed the initial mean ± 2 times the initial standard deviation to obtain the remaining pixels; recalculating the region mean and standard deviation based on the gray values ​​of the remaining pixels; determining a preset threshold based on the region mean and standard deviation, and binarizing the raw astronomical image data according to the preset threshold to obtain a binarized image corresponding to the raw astronomical image data.

[0009] The method provided by this invention clarifies the specific steps of binarization processing. By selecting a continuous region that accounts for a preset proportion of the total number of pixels, calculating the initial mean and standard deviation, removing abnormal pixels, recalculating the mean and standard deviation of the pixel grayscale values ​​of the remaining pixels, and then performing binarization, the objectivity and rationality of the preset threshold are ensured, avoiding the influence of the subjectivity of the threshold setting on the binarization effect. This ensures the accuracy of the bright areas in the binarized image and lays a good foundation for subsequent connected component analysis.

[0010] In one possible implementation of the first aspect, connected component analysis is performed on the bright regions included in the binarized image to obtain multiple connected components. The multiple connected components are then filtered based on their area and roundness to obtain a connected component list. This includes: extracting the contours of the bright regions in the binarized image to obtain contour information of the multiple connected components; constructing an initial connected component list based on the areas of the multiple connected components, wherein the area of ​​each connected component in the initial connected component list is within a preset area range; calculating the roundness of each connected component in the initial connected component list, where the roundness is the ratio of the longer side to the shorter side of the smallest bounding rectangle of the connected component; and removing connected components whose roundness is greater than the preset ratio to obtain the connected component list.

[0011] The method provided by this invention first extracts the outline of the bright region and constructs an initial connected component list according to the area during the connected component analysis process. Then, by calculating the roundness (the ratio of the major axis to the minor axis) and eliminating connected components that exceed the preset ratio, it can focus on the candidate regions of stars with higher brightness and eliminate the interference of elongated non-stars. This reduces the workload of subsequent two-dimensional Gaussian fitting calculations, improves the screening efficiency, and at the same time initially ensures the effectiveness of the connected component list.

[0012] In one possible implementation of the first aspect, the step of performing a two-dimensional Gaussian fitting calculation on each connected component included in the connected component list to obtain parameter information corresponding to each connected component includes: calculating the geometric center coordinates (x0, y0) of each connected component; extracting sub-image blocks with a preset radius R0 based on the geometric center coordinates (x0, y0), wherein the sub-image blocks contain pixel coordinates (x, y) and corresponding pixel gray values ​​for fitting; and performing a two-dimensional Gaussian fitting calculation on the pixel coordinates and gray values ​​of the sub-image blocks using the least squares method to obtain parameter information corresponding to each connected component. The formula for the two-dimensional Gaussian fitting is: ; Where α represents the fitted star flux peak value, β represents the fitted background value, and (x, y) are the pixel positions used for fitting. c ,y c ) represents the coordinates of the star image center obtained through two-dimensional Gaussian fitting, and σ is the coordinates of the star image center.x and σ y These are the standard deviations of the pixel coordinates in the x and y directions, respectively.

[0013] The method provided by this invention first determines the geometric center coordinates of the connected components and extracts sub-image blocks according to a preset radius in the two-dimensional Gaussian fitting calculation. Then, it uses the least squares method to fit the pixel coordinates and gray values. Furthermore, it clarifies the definition of each parameter in the two-dimensional Gaussian fitting formula, which can accurately obtain key parameter information such as the peak flux of star images, background values, and star image center coordinates of each connected component. This provides accurate data support for subsequent parameter-based multi-round elimination and screening, ensuring the reliability of the screening results.

[0014] In one possible implementation of the first aspect, the preset screening conditions include standard deviation conditions, background correlation conditions, fitting error conditions, grayscale peak conditions, and boundary distance conditions; the multi-round elimination screening based on the preset screening conditions includes: based on the standard deviation conditions, eliminating those satisfying e1 < (σ x +σ y Connected components satisfying 1 / 2 < e2, where e1 and e2 are standard deviation range thresholds; based on the background correlation condition, connected components satisfying bn×bsigma < β < b+n×bsigma are removed, where b is the region mean during binarization, b+n×bsigma is the region standard deviation during binarization, and n is a preset multiplier; based on the fitting error condition, connected components satisfying δ x >e x And δ y >e y The connected components, where e x e is the center fitting error threshold in the x-direction of pixel coordinates. y δ is the center fitting error threshold in the y-direction of pixel coordinates. x and δ y These are the fitting errors of the star image center coordinates in the x-axis and y-axis of pixels, respectively; based on the grayscale peak value condition, connected components satisfying α > h are removed, where h is the product of the maximum grayscale value of the original astronomical image data and a preset ratio; based on the boundary distance condition, connected components satisfying the star image center coordinates (x...h ... c ,y c Connected components whose distance to the left, right, top, or bottom boundary of the image is less than p, where p is a preset distance threshold.

[0015] The method provided by this invention clarifies the preset screening conditions, including standard deviation conditions, background correlation conditions, fitting error conditions, grayscale peak conditions, and boundary distance conditions. Specific elimination rules and values ​​are set for each condition. Through multiple rounds of elimination, interference items such as cosmic rays, crowded star images, false outlines, star images significantly affected by noise, dark stars, saturated star images, and boundary stars can be eliminated. This makes the final screened star images more in line with the high-quality requirements of being isolated, unsaturated, and unaffected by noise, further improving the accuracy of star image screening and ensuring the accuracy of subsequent image quality evaluation index calculations.

[0016] Secondly, the present invention provides a star image screening system for evaluating astronomical images from space telescopes. The system includes: an image acquisition module for acquiring raw astronomical image data observed by a space telescope; a binarization processing module for binarizing the raw astronomical image data to obtain a binarized image corresponding to the raw astronomical image data, wherein the binarized image includes bright regions with pixel grayscale values ​​greater than or equal to a preset threshold and dark regions with pixel grayscale values ​​less than a preset threshold; and a connected component analysis module for performing connected component analysis on the bright regions included in the binarized image to obtain multiple connected components. The connected components are filtered based on their area and roundness to obtain a list of connected components. A fitting calculation module performs a two-dimensional Gaussian fitting calculation on each connected component in the list to obtain parameter information corresponding to each connected component. The parameter information includes the star flux peak value, background value, star center coordinates, x-direction standard deviation, y-direction standard deviation, and center coordinate fitting error. A rejection filtering module performs multiple rounds of rejection filtering based on preset filtering conditions and the parameter information corresponding to each connected component to obtain a star image rejection result, which includes multiple rejected star images.

[0017] In one possible implementation of the second aspect, the binarization processing module is specifically used for: selecting a continuous region in the original astronomical image data that accounts for a preset proportion of the total number of pixels; calculating the initial mean and initial standard deviation of the gray values ​​of all pixels in each continuous region; removing pixels that exceed the initial mean ± 2 times the initial standard deviation to obtain the remaining pixels; recalculating the region mean and standard deviation based on the gray values ​​of the remaining pixels; determining a preset threshold based on the region mean and standard deviation, and binarizing the original astronomical image data according to the preset threshold to obtain the binarized image corresponding to the original astronomical image data.

[0018] In one possible implementation of the second aspect, the preset screening conditions include standard deviation conditions, background correlation conditions, fitting error conditions, grayscale peak conditions, and boundary distance conditions; the elimination screening module is specifically used to: based on the standard deviation conditions, eliminate those satisfying e1 < (σ x +σy Connected components satisfying 1 / 2 < e2, where e1 and e2 are standard deviation range thresholds; based on the background correlation condition, connected components satisfying bn×bsigma < β < b+n×bsigma are removed, where b is the region mean during binarization, b+n×bsigma is the region standard deviation during binarization, and n is a preset multiplier; based on the fitting error condition, connected components satisfying δ x >e x And δ y >e y The connected components, where e x e is the center fitting error threshold in the x-direction of pixel coordinates. y δ is the center fitting error threshold in the y-direction of pixel coordinates. x and δ y These are the fitting errors of the star image center coordinates in the x-axis and y-axis of pixels, respectively; based on the grayscale peak value condition, connected components satisfying α > h are removed, where h is the product of the maximum grayscale value of the original astronomical image data and a preset ratio; based on the boundary distance condition, connected components satisfying the star image center coordinates (x...h ... c ,y c Connected components whose distance to the left, right, top, or bottom boundary of the image is less than p, where p is a preset distance threshold.

[0019] Thirdly, an electronic device is provided, the electronic device including a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the method as described in any implementation of the first aspect.

[0020] Fourthly, a computer-readable storage medium is provided, including computer instructions that, when executed on an electronic device, cause the electronic device to perform a method as described in any implementation of the first aspect.

[0021] Fifthly, a computer program product is provided that, when run on a computer, causes the computer to perform the method in any implementation of the first aspect.

[0022] Understandably, the beneficial effects achieved by the system of the second aspect, the electronic device of the third aspect, the computer-readable storage medium of the fourth aspect, and the computer program product of the fifth aspect provided above can be referred to with reference to the beneficial effects of the first aspect and any of its possible design embodiments, which will not be repeated here. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention; Figure 2 A flowchart of a star image selection method for evaluating astronomical images from a space telescope, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of raw astronomical image data A provided in an embodiment of the present invention; Figure 4 A schematic diagram of raw astronomical image data B provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of star image filtering results corresponding to raw astronomical image data A provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of star image filtering results corresponding to raw astronomical image data B provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of a star image screening system provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. The "or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A or B can represent: A alone, A and B simultaneously, and B alone. A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.

[0025] Furthermore, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0026] In this embodiment of the invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in this embodiment of the invention should not be construed as superior or more advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0027] In the field of optical astronomical image processing in astronomy, large-scale sky surveys by space telescopes generate a massive number of astronomical images. The operation and control site needs to quickly evaluate the image quality, focus, and pointing of these images to ensure the quality of observation and adjust the observation parameters in a timely manner. As the core link in the evaluation process, the efficiency and accuracy of star image selection directly affect the reliability and timeliness of the overall evaluation results.

[0028] Currently, the industry commonly uses Gaussian template detection to detect star images. This technology requires convolution calculation of connected components, and then screening of connected components by calculating morphological features such as roundness, sharpness, and peak gray value, thereby separating out star images that meet the requirements for subsequent index calculations.

[0029] However, for space telescope astronomical images, this method of star detection and screening cannot meet the needs of rapid evaluation of image quality, focus, and pointing at the telescope operation and control site. This is mainly because the convolution calculation is time-consuming, the success rate of detecting cosmic rays based on the above-mentioned morphological features is not high, and it cannot effectively remove saturated stars and galaxies. It cannot simultaneously meet the needs of star image quality evaluation and focus and pointing evaluation based on astrometric calculations, thus affecting the timeliness and accuracy of rapid evaluation of astronomical images.

[0030] In view of this, embodiments of the present invention provide a method and system for star image selection for evaluation of astronomical images from space telescopes, the method comprising: acquiring raw astronomical image data observed by a space telescope; The raw astronomical image data is binarized to obtain a binarized image. This binarized image includes bright regions with pixel grayscale values ​​greater than or equal to a preset threshold and dark regions with pixel grayscale values ​​less than a preset threshold. Connected component analysis is performed on the bright regions of the binarized image to obtain multiple connected components. These connected components are then filtered based on their area and roundness to obtain a list of connected components. A two-dimensional Gaussian fitting calculation is performed on each connected component in the list to obtain parameter information corresponding to each component. This parameter information includes the star flux peak value, background value, star center coordinates, x-direction standard deviation, y-direction standard deviation, and center coordinate fitting error. Based on preset filtering conditions, multiple rounds of elimination and filtering are performed according to the parameter information corresponding to each connected component to obtain a star image filtering result, which includes multiple filtered star images.

[0031] The method provided by this invention systematically achieves star image screening through a complete process of acquiring raw astronomical image data, binarization processing, connected component analysis, two-dimensional Gaussian fitting calculation, and multi-round elimination screening. It clarifies the specific composition of the bright and dark part classification features of the binarized image, the core information of the connected component list, and the parameter information. Furthermore, by performing multi-round elimination through preset screening conditions, it ensures the targetedness and effectiveness of the selected star images. This solves the problem of the lack of a unified and complete process for star image screening in the evaluation of space telescope astronomical images, providing a reliable star image basis for subsequent image quality, focus, and pointing assessments. This invention enables rapid star image screening of space telescope astronomical images, reduces computational costs, and improves adaptability to complex situations such as saturated star images, galaxies, aliased star images, and edge star images, thereby enhancing the accuracy and timeliness of astronomical image evaluation.

[0032] In some embodiments, the star image selection method for evaluating astronomical images from space telescopes provided in this invention can be executed by a star image selection system 100 for evaluating astronomical images from space telescopes (hereinafter referred to as star image selection system 100).

[0033] As an example, the star chart selection system 100 can be any electronic device 200 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer. The specific implementation of the star chart selection system 100 is not limited here.

[0034] Figure 1 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown. The electronic device 200 includes a processor 210, a memory 220, and a communication interface 230.

[0035] Processor 210 may include one or more processing cores. Processor 210 connects to various parts within electronic device 200 using various interfaces and lines, and performs various functions and processes data of electronic device 200 by running or executing instructions, programs, code sets, or instruction sets stored in memory 220, and by calling data stored in memory 220. Optionally, processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0036] The memory 220 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 220 may include a non-transitory computer-readable storage medium. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a program storage area. This program storage area may store instructions for implementing an operating system, instructions for implementing at least one function, instructions for implementing the various method embodiments described above, etc.

[0037] Communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing devices or Ethernet, wireless access network (RAN), wireless local area network (WLAN), etc.

[0038] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, and communication interface 230) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be located within the same device, i.e., as different devices within the same device, similar to the deployment of devices or components in a distributed system.

[0039] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 200. In other embodiments of the present invention, the electronic device 200 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0040] The following description, in conjunction with the accompanying drawings, illustrates a star image selection method for evaluating astronomical images from a space telescope, provided by an embodiment of the present invention.

[0041] Figure 2 This is a flowchart illustrating a star image selection method for evaluating astronomical images from a space telescope, provided as an embodiment of the present invention. Optionally, this method can be... Figure 1 The illustrated electronic device 200 performs this method, which includes the following steps: S1. Acquire raw astronomical image data from space telescope observations.

[0042] For example, see Figure 3 and Figure 4These are astronomical raw image data A and astronomical raw image data B, respectively, provided in an embodiment of the present invention. Astronomical raw image data A and astronomical raw image data B are FITS image data read from a CCD.

[0043] S2. The original astronomical image data is binarized to obtain the binarized image corresponding to the original astronomical image data.

[0044] The binarized image includes bright areas with pixel grayscale values ​​greater than or equal to a preset threshold and dark areas with pixel grayscale values ​​less than a preset threshold.

[0045] In some embodiments, S2 includes: Select a continuous region in the original astronomical image data that accounts for a preset proportion of the total number of pixels; calculate the initial mean and initial standard deviation of the gray values ​​of all pixels in each continuous region; remove pixels that exceed the initial mean ± 2 times the initial standard deviation to obtain the remaining pixels; recalculate the region mean and standard deviation based on the pixel gray values ​​of the remaining pixels; determine a preset threshold based on the region mean and standard deviation, and binarize the original astronomical image data according to the preset threshold to obtain the binarized image corresponding to the original astronomical image data.

[0046] For example, the specific process of binarization is as follows: After removing pixels in a certain region of the image by 2 sigma, calculate the mean b and standard deviation bsigma. To improve the processing speed, a small continuous region of the image is usually calculated, for example, occupying one-twentieth of the image pixels. Using n1 times bsigma as the threshold, different regions of the entire image are binarized separately to obtain the binarized image corresponding to each region. Then, the binarized images corresponding to each region are stitched together to obtain the binarized image of the entire image.

[0047] It should be noted that the regional mean and standard deviation can be recalculated multiple times, for example, up to three times. This embodiment of the invention does not impose any particular limitation on this.

[0048] For example, the preset ratio is 1 / 20. It should be understood that the preset ratio can be flexibly set according to the actual use scenario, and the embodiments of the present invention do not impose any special limitations on this.

[0049] The method provided by this invention clarifies the specific steps of binarization processing. By selecting a continuous region that accounts for a preset proportion of the total number of pixels, calculating the initial mean and standard deviation, removing abnormal pixels, recalculating the mean and standard deviation of the pixel grayscale values ​​of the remaining pixels, and then performing binarization, the objectivity and rationality of the preset threshold are ensured, avoiding the influence of the subjectivity of the threshold setting on the binarization effect. This ensures the accuracy of the bright areas in the binarized image and lays a good foundation for subsequent connected component analysis.

[0050] S3. Perform connected component analysis on the bright areas included in the binarized image to obtain multiple connected components. Filter the multiple connected components according to the area and roundness of each connected component to obtain a list of connected components.

[0051] In some embodiments, S3 above includes: extracting the contours of the bright regions in the binarized image to obtain the contour information of multiple connected components; An initial connected component list is constructed based on the areas of the multiple connected components, wherein the area of ​​each connected component in the initial connected component list is within a preset area range. Calculate the roundness of each connected component in the initial connected component list, where the roundness is the ratio of the length of the longer side to the length of the shorter side of the smallest bounding rectangle of the connected component; The connected components with a roundness greater than a preset ratio are removed to obtain the list of connected components.

[0052] For example, the preset area range is greater than or equal to 5 and less than or equal to 200, and the preset ratio is 2.

[0053] The aforementioned S3 specifically includes: The contours of the binarized image are sorted according to the sum of pixel gray values ​​within their corresponding areas after subtracting the mean of the background gray values. Only the 100 connected components with the largest sum of pixel values ​​are retained in the list. The ratio R of the major axis to the minor axis is calculated for each connected component in the list. Connected components with a ratio greater than 2 are removed from the list, resulting in the connected component list.

[0054] The method provided by this invention first extracts the outline of the bright area during connected component analysis and sorts it in descending order according to the sum of pixel gray values. It retains the N connected components with the largest sum of pixel gray values ​​to construct an initial connected component list. Then, by calculating the ratio of the major axis to the minor axis and removing connected components that exceed the preset ratio, it can focus on the candidate areas of stars with higher brightness and eliminate the interference of slender non-stars. This reduces the workload of subsequent two-dimensional Gaussian fitting calculations, improves the screening efficiency, and at the same time initially ensures the effectiveness of the connected component list.

[0055] S4. Perform a two-dimensional Gaussian fitting calculation on each connected component in the connected component list to obtain the parameter information corresponding to each connected component.

[0056] The parameter information includes the peak value of star flux, background value, star center coordinates, standard deviation in the x-direction, standard deviation in the y-direction, and center coordinate fitting error.

[0057] In one possible implementation, the step of performing a two-dimensional Gaussian fitting calculation on each connected component in the connected component list to obtain parameter information corresponding to each connected component includes: calculating the geometric center coordinates (x0, y0) of each connected component; extracting sub-image blocks with a preset radius R0 based on the geometric center coordinates (x0, y0), wherein the sub-image blocks contain pixel coordinates (x, y) and corresponding pixel gray values ​​for fitting; and performing a two-dimensional Gaussian fitting calculation on the pixel coordinates and gray values ​​of the sub-image blocks using the least squares method to obtain parameter information corresponding to each connected component. The formula for the two-dimensional Gaussian fitting is: ; Where α represents the fitted star flux peak value, β represents the fitted background value, and (x, y) are the pixel positions used for fitting. c ,y c ) represents the coordinates of the star image center obtained through two-dimensional Gaussian fitting, and σ is the coordinates of the star image center. x and σ y These are the standard deviations of the pixel coordinates in the x and y directions, respectively.

[0058] For example, R0 is 15.

[0059] S5. Based on preset filtering conditions, multiple rounds of elimination filtering are performed according to the parameter information corresponding to each connected component to obtain star image filtering results, which include multiple filtered star images.

[0060] In some embodiments, the preset screening conditions include standard deviation conditions, background correlation conditions, fitting error conditions, grayscale peak conditions, and boundary distance conditions.

[0061] Furthermore, the aforementioned S5 specifically includes: Based on the aforementioned standard deviation condition, items satisfying e1 < (σ) are excluded. x +σ y The connected components of ) / 2 < e2, where e1 and e2 are the standard deviation range thresholds; Based on the aforementioned background association conditions, connected components that satisfy bn×bsigma<β<b+n×bsigma are removed, where b is the region mean during binarization, b+n×bsigma is the region standard deviation during binarization, and n is a preset multiplier. Based on the aforementioned fitting error condition, those satisfying δ are eliminated. x >e x And δ y >e y The connected components, where e x e is the center fitting error threshold in the x-direction of pixel coordinates. y δ is the center fitting error threshold in the y-direction of pixel coordinates.x and δ y These are the fitting errors of the star image center coordinates in the pixel x and pixel y directions, respectively; Based on the grayscale peak condition, connected components that satisfy α > h are removed, where h is the product of the maximum grayscale value of the original astronomical image data and a preset ratio. Based on the aforementioned boundary distance condition, stars satisfying the coordinates (x) of the star image center are excluded. c ,y c Connected components whose distance to the left, right, top, or bottom boundary of the image is less than p, where p is a preset distance threshold.

[0062] In one example, based on the fitting error condition, items satisfying e1 < (σ) are removed. x +σ y The connected components of ) / 2 < e2, where e1 is 0.5 and e2 is 2.0; Based on the aforementioned background association conditions, connected components that satisfy bn×bsigma<β<b+n×bsigma are removed, where b is the region mean during binarization, bsigma is the region standard deviation during binarization, and n is 10. Based on the aforementioned fitting error condition, those satisfying δ are eliminated. x >e x And δ y > ey's connected components, where e x and e y The value of is 0.3; Based on the aforementioned grayscale peak value condition, connected components satisfying α > h are removed, where h is 90% of the maximum grayscale value of the original astronomical image data; Based on the boundary distance condition, connected components that satisfy the condition that the distance between the star image center coordinates (xc, yc) and the left, right, top, or bottom boundary of the image is less than p are removed, where p is 20 pixels.

[0063] The method provided by this invention clarifies the preset screening conditions, including fitting error conditions, background correlation conditions, standard deviation conditions, grayscale peak conditions, and boundary distance conditions. Specific elimination rules and values ​​are set for each condition. Through multiple rounds of elimination, interference items such as cosmic rays, crowded star images, false outlines, star images significantly affected by noise, dark stars, saturated star images, and boundary stars can be eliminated. This makes the final screened star images more in line with the high-quality requirements of being isolated, unsaturated, and unaffected by noise, further improving the accuracy of star image screening and ensuring the accuracy of subsequent image quality evaluation index calculations.

[0064] As shown in S1-S5, the method provided by this invention systematically achieves star image screening through a complete process of acquiring raw astronomical image data, binarization processing, connected component analysis, two-dimensional Gaussian fitting calculation, and multi-round elimination screening. It clarifies the specific composition of the bright and dark part classification features of the binarized image, the core information of the connected component list, and the parameter information. Furthermore, by performing multi-round elimination through preset screening conditions, it ensures the targetedness and effectiveness of the selected star images. This solves the problem of the lack of a unified and complete process for star image screening in space telescope astronomical image evaluation, providing a reliable star image basis for subsequent image quality, focus, and pointing evaluation. This invention can quickly screen star images from space telescope astronomical images, reduce computational costs, and improve adaptability to complex situations such as saturated star images, galaxies, aliased star images, and edge star images, thereby enhancing the accuracy and timeliness of astronomical image evaluation.

[0065] It can also be understood that the beneficial effects of the method provided by the embodiments of the present invention are as follows: The present invention constructs a full-process, fully automatic star image screening method for large-format, multi-CCD raw imaging data, which can meet the needs of real-time image processing while maintaining high detection accuracy, significantly overcomes the limitations of traditional technology in complex observation scenarios, and realizes rapid screening of isolated, unsaturated high signal-to-noise ratio star images in massive optical astronomical images.

[0066] To facilitate understanding of this solution, the following specific example will be used to further explain and illustrate the method provided in the embodiments of the present invention.

[0067] In one example, the method provided by this embodiment of the invention includes: First, acquire raw astronomical image data observed by the space telescope. This data is image data from a flexible image transmission system with multiple channels readout from a charge-coupled device. The image pixel size is 512×512 pixels, and the grayscale value range is 0 to 65535.

[0068] Then, the preprocessed image is binarized. A continuous region accounting for one-twentieth of the total pixels is selected, and the initial mean of all pixels in this region is calculated to be 500, with an initial standard deviation of 8. After removing outlier pixels exceeding 500±16 (twice the initial standard deviation), the region mean b=400 and standard deviation bsigma=6 are recalculated based on the remaining pixels. Using n1=twice bsigma (i.e., 12) as a preset threshold, binarized images of different continuous regions are obtained. Then, the binarized images of different regions are stitched together to obtain the binarized image of the entire image. Among them, pixel gray values ​​greater than or equal to 492 are considered bright areas, and those less than 492 are considered dark areas.

[0069] Subsequently, connected component analysis was performed on the bright areas of the binarized image to extract the contour information of all bright areas, resulting in 156 connected components. These 156 connected components were then filtered according to a preset area range, removing those whose areas did not fall within the preset range, resulting in 130 connected components. The preset area range is greater than or equal to 5 and less than or equal to 200. These 130 connected components were then sorted in descending order based on the sum of the pixel gray values ​​within their respective areas after subtracting the mean background gray value of the corresponding region, retaining the connected components. An initial list of connected components (N=100) is constructed from the 100 connected components with the largest sum of pixel grayscale values ​​within their connected area. The ratio of the major axis to the minor axis (the ratio of the length of the long side to the length of the short side of the smallest bounding rectangle) of each connected component is calculated. Twelve connected components with a ratio greater than 2 are removed, resulting in a final list of 88 connected components. Each connected component in the list records its area and contour coordinate information. For example, one connected component has an area of ​​120 pixels and contour coordinates ranging from x1=100 to x2=115 and y1=200 to y2=208.

[0070] Next, a two-dimensional Gaussian fitting calculation is performed on each connected component in the connected component list. First, the geometric center coordinates of each connected component are calculated. For example, the geometric center coordinates of the connected component with an area of ​​120 pixels are (x0=107.5, y0=204). Using this center as a reference, a sub-image block is extracted with a preset radius R0=15 pixels. The sub-image block contains pixel coordinates (x, y) and the corresponding pixel gray value. For example, the gray value corresponding to coordinates (105, 202) is 78. The least squares method is used to perform a two-dimensional Gaussian fitting on these pixel coordinates and gray values ​​to obtain the parameter information of the connected component: star flux peak α=8500, background value β=420, star center coordinates (x0=107.5, y0=204). c =107.318,y c =204.125), standard deviation σ in the x-direction x =0.25, y-direction standard deviation σ y =0.22, center coordinate fitting error δ x =0.03、δ y =0.02, and the remaining 87 connected components all obtained their corresponding complete parameter information in the same way.

[0071] Finally, based on preset screening conditions, multiple rounds of elimination were conducted. Under the condition of fitting error, those that satisfy 0.5 < (σ) were eliminated. x +σ y For a connected component whose σ / 2 < 2.0, the above connected component (σ) x +σ y) / 2=0.235, retained; under the background correlation condition, disconnected connected components that do not satisfy 400-10×6<β<400+10×6 (i.e. 340<β<460) are removed. The above connected components with β=420 are retained; under the standard deviation condition, components that satisfy δ y >0.3 and δ y For connected components with a δ > 0.3, the above connected component δ x =0.03、δ y =0.02, retained; under the grayscale peak condition, connected components satisfying α>65535×90% (i.e. 58981.5) are removed; under the boundary distance condition, connected components whose center coordinates are less than 20 pixels from the image boundary are removed. The center of the above connected components with a distance of 107.318 pixels from the left boundary, 404.682 pixels from the right boundary, 204.125 pixels from the top boundary, and 307.875 pixels from the bottom boundary are retained. After multiple rounds of removal, the final star image selection result contains 69 selected star images. These star images are all isolated, unsaturated, and high-quality star images that are not significantly affected by noise, and can be directly used for rapid evaluation of subsequent image quality, focus, and other indicators.

[0072] The foregoing mainly describes the solutions of the embodiments of the present invention from a methodological perspective. It is understood that, in order to achieve the above-mentioned functions, the star-viewing system 100 includes at least one of the hardware structures and software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present invention.

[0073] In this embodiment of the invention, the star chart selection system 100 can be divided into functional units according to the above method example. For example, the star chart selection system 100 can be divided into functional units corresponding to each function, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this embodiment of the invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0074] For example, Figure 7This diagram illustrates the hardware structure of a star image selection system according to an embodiment of the present invention. The star image selection system 100 includes: an image acquisition module 110 for acquiring raw astronomical image data observed by a space telescope; a binarization processing module 120 for performing binarization processing on the raw astronomical image data to obtain a binarized image corresponding to the raw astronomical image data, the binarized image including bright areas with pixel grayscale values ​​greater than or equal to a preset threshold and dark areas with pixel grayscale values ​​less than a preset threshold; and a connected component analysis module 130 for performing connected component analysis on the bright areas included in the binarized image to obtain multiple connected components, and for analyzing the area and roundness of each connected component. The multiple connected components are filtered to obtain a list of connected components; the fitting calculation module 140 is used to perform two-dimensional Gaussian fitting calculation on each connected component in the list of connected components to obtain parameter information corresponding to each connected component. The parameter information includes the peak value of star flux, background value, star center coordinates, standard deviation in the x-direction, standard deviation in the y-direction, and fitting error of the center coordinates; the elimination filtering module 150 is used to perform multiple rounds of elimination filtering based on preset filtering conditions and according to the parameter information corresponding to each connected component to obtain star image filtering results. The star image filtering results include multiple filtered star images.

[0075] Optionally, the binarization processing module 120 is specifically used for: selecting a continuous region in the original astronomical image data that accounts for a preset proportion of the total number of pixels; calculating the initial mean and initial standard deviation of the gray values ​​of all pixels in each continuous region; removing pixels that exceed the initial mean ± 2 times the initial standard deviation to obtain the remaining pixels; recalculating the region mean and standard deviation based on the gray values ​​of the remaining pixels; determining a preset threshold based on the region mean and standard deviation, and binarizing the original astronomical image data according to the preset threshold to obtain the binarized image corresponding to the original astronomical image data.

[0076] Optionally, the preset screening conditions include fitting standard deviation conditions, background correlation conditions, fitting error conditions, grayscale peak conditions, and boundary distance conditions; the elimination screening module 150 is specifically used to: based on the standard deviation conditions, eliminate those that satisfy e1 < (σ x +σ y Connected components satisfying 1 / 2 < e2, where e1 and e2 are standard deviation range thresholds; based on the background correlation condition, connected components satisfying bn×bsigma < β < b+n×bsigma are removed, where b is the region mean during binarization, b+n×bsigma is the region standard deviation during binarization, and n is a preset multiplier; based on the fitting error condition, connected components satisfying δ x >e x And δ y >e y The connected components, where e xe is the center fitting error threshold in the x-direction of pixel coordinates. y The center fitting error threshold is set to the pixel coordinate y-direction; based on the grayscale peak value condition, connected components satisfying α > h are removed, where h is the product of the maximum grayscale value of the original astronomical image data and a preset ratio; based on the boundary distance condition, components satisfying the star image center coordinate (x...h ... c ,y c Connected components whose distance to the left, right, top, or bottom boundary of the image is less than p, where p is a preset distance threshold.

[0077] It should be understood that specific descriptions of the above-mentioned optional methods can be found in the foregoing method embodiments, and will not be repeated here. Furthermore, explanations of any of the star chart selection systems 100 provided above, as well as descriptions of their beneficial effects, can be found in the corresponding method embodiments described above, and will not be repeated here.

[0078] This invention also provides a computer-readable storage medium storing at least one computer instruction, which is loaded and executed by a processor to implement the methods of the various embodiments described above. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0079] This invention also provides a chip. This chip integrates a control circuit for implementing the functions of the aforementioned star-viewing system 100 and one or more ports. Optionally, the functions supported by this chip are as described above and will not be repeated here.

[0080] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0081] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0082] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0083] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for star image selection in the evaluation of astronomical images from space telescopes, characterized in that, The method includes: To acquire raw astronomical image data from space telescope observations; The original astronomical image data is binarized to obtain a binarized image corresponding to the original astronomical image data. The binarized image includes bright areas with pixel gray values ​​greater than or equal to a preset threshold and dark areas with pixel gray values ​​less than a preset threshold. Connectivity analysis is performed on the bright regions included in the binarized image to obtain multiple connected components. The multiple connected components are then filtered according to the area and roundness of each connected component to obtain a list of connected components. Two-dimensional Gaussian fitting calculation is performed on each connected component in the list of connected components to obtain the parameter information corresponding to each connected component. The parameter information includes the peak value of star flux, background value, star center coordinates, standard deviation in the x direction, standard deviation in the y direction, and center coordinate fitting error. Based on preset filtering conditions, multiple rounds of elimination filtering are performed according to the parameter information corresponding to each connected component to obtain star image filtering results, which include multiple filtered star images.

2. The star image selection method for evaluating astronomical images from space telescopes according to claim 1, characterized in that, The raw astronomical image data is binarized, including: Select a continuous region in the original astronomical image data that accounts for a preset proportion of the total number of pixels; Calculate the initial mean and initial standard deviation of the grayscale values ​​of all pixels within each continuous region; Remove pixels that exceed the initial mean ± 2 times the initial standard deviation to obtain the remaining pixels; The mean and standard deviation of the region are recalculated based on the gray values ​​of the remaining pixels; A preset threshold is determined based on the regional mean and standard deviation, and the original astronomical image data is binarized according to the preset threshold to obtain the binarized image corresponding to the original astronomical image data.

3. The star image selection method for evaluating astronomical images from space telescopes according to claim 1, characterized in that, Connectivity analysis is performed on the bright regions of the binarized image to obtain multiple connected components. These connected components are then filtered based on their area and roundness to obtain a list of connected components, including: Contour extraction is performed on the bright areas in the binarized image to obtain contour information of multiple connected components; An initial connected component list is constructed based on the areas of the multiple connected components, wherein the area of ​​each connected component in the initial connected component list is within a preset area range. Calculate the roundness of each connected component in the initial connected component list, where the roundness is the ratio of the length of the longer side to the length of the shorter side of the smallest bounding rectangle of the connected component; The connected components with a roundness greater than a preset ratio are removed to obtain the list of connected components.

4. The star image selection method for evaluating astronomical images from space telescopes according to claim 1, characterized in that, The step of performing a two-dimensional Gaussian fitting calculation on each connected component in the connected component list to obtain the parameter information corresponding to each connected component includes: Calculate the geometric center coordinates (x0, y0) of each connected component; Based on the geometric center coordinates (x0, y0), sub-image blocks are extracted with a preset radius R0. The sub-image blocks contain pixel coordinates (x, y) and corresponding pixel gray values ​​for fitting. The pixel coordinates and gray values ​​of the sub-image blocks are calculated using a two-dimensional Gaussian fitting method with the least squares method to obtain the parameter information corresponding to each connected component; The formula for the two-dimensional Gaussian fitting is: ； Where α represents the fitted star flux peak value, β represents the fitted background value, and (x, y) are the pixel positions used for fitting. c ,y c ) represents the coordinates of the star image center obtained through two-dimensional Gaussian fitting, and σ is the coordinates of the star image center. x and σ y These are the standard deviations of the pixel coordinates in the x and y directions, respectively.

5. The star image selection method for evaluating astronomical images from space telescopes according to claim 4, characterized in that, The preset screening conditions include standard deviation condition, background correlation condition, fitting error condition, grayscale peak value condition, and boundary distance condition; The multi-round elimination screening based on preset screening conditions includes: Based on the aforementioned standard deviation condition, items satisfying e1 < (σ) are excluded. x +σ y The connected components of ) / 2 < e2, where e1 and e2 are the standard deviation range thresholds; Based on the aforementioned background association conditions, connected components that satisfy bn×bsigma<β<b+n×bsigma are removed, where b is the region mean during binarization, b+n×bsigma is the region standard deviation during binarization, and n is a preset multiplier. Based on the aforementioned fitting error condition, those satisfying δ are eliminated. x >e x And δ y >e y The connected components, where e x e is the center fitting error threshold in the x-direction of pixel coordinates. y δ is the center fitting error threshold in the y-direction of pixel coordinates. x and δ y These are the fitting errors of the star image center coordinates in the pixel x and pixel y directions, respectively; Based on the grayscale peak condition, connected components that satisfy α > h are removed, where h is the product of the maximum grayscale value of the original astronomical image data and a preset ratio. Based on the aforementioned boundary distance condition, stars satisfying the coordinates (x) of the star image center are excluded. c ,y c Connected components whose distance to the left, right, top, or bottom boundary of the image is less than p, where p is a preset distance threshold.

6. A star image selection system for evaluating astronomical images from space telescopes, characterized in that, The system includes: The image acquisition module is used to acquire raw astronomical image data from space telescope observations; The binarization processing module is used to perform binarization processing on the original astronomical image data to obtain a binarized image corresponding to the original astronomical image data. The binarized image includes bright areas with image pixel gray values ​​greater than or equal to a preset threshold and dark areas with gray values ​​less than a preset threshold. The connected component analysis module is used to perform connected component analysis on the bright regions included in the binarized image to obtain multiple connected components. The multiple connected components are then filtered according to the area and roundness of each connected component to obtain a list of connected components. The fitting calculation module is used to perform two-dimensional Gaussian fitting calculation on each connected component included in the connected component list to obtain the parameter information corresponding to each connected component. The parameter information includes the star image flux peak value, background value, star image center coordinates, x-direction standard deviation, y-direction standard deviation and center coordinate fitting error. The elimination and filtering module is used to perform multiple rounds of elimination and filtering based on preset filtering conditions and parameter information corresponding to each connected component to obtain star image filtering results, which include multiple filtered star images.

7. The star image selection system for evaluating astronomical images from space telescopes according to claim 6, characterized in that, The binarization processing module is specifically used for: Select a continuous region in the original astronomical image data that accounts for a preset proportion of the total number of pixels; Calculate the initial mean and initial standard deviation of the grayscale values ​​of all pixels within each continuous region; Remove pixels that exceed the initial mean ± 2 times the initial standard deviation to obtain the remaining pixels; The mean and standard deviation of the region are recalculated based on the gray values ​​of the remaining pixels; A preset threshold is determined based on the regional mean and standard deviation, and the original astronomical image data is binarized according to the preset threshold to obtain the binarized image corresponding to the original astronomical image data.

8. The star image selection system for evaluating astronomical images from space telescopes according to claim 6, characterized in that, The preset screening conditions include standard deviation condition, background correlation condition, fitting error condition, grayscale peak value condition, and boundary distance condition; The rejection and filtering module is specifically used for: Based on the aforementioned standard deviation condition, items satisfying e1 < (σ) are excluded. x +σ y The connected components of ) / 2 < e2, where e1 and e2 are the standard deviation range thresholds; Based on the aforementioned background association conditions, connected components that satisfy bn×bsigma<β<b+n×bsigma are removed, where b is the region mean during binarization, b+n×bsigma is the region standard deviation during binarization, and n is a preset multiplier. Based on the aforementioned fitting error condition, those satisfying σ are eliminated. x >e x And σ y >e y The connected components, where e x e is the center fitting error threshold in the x-direction of pixel coordinates. y δ is the center fitting error threshold in the y-direction of pixel coordinates. x and δ y These are the fitting errors of the star image center coordinates in the pixel x and pixel y directions, respectively; Based on the grayscale peak condition, connected components that satisfy α > h are removed, where h is the product of the maximum grayscale value of the original astronomical image data and a preset ratio. Based on the aforementioned boundary distance condition, stars satisfying the coordinates (x) of the star image center are excluded. c ,y c Connected components whose distance to the left, right, top, or bottom boundary of the image is less than p, where p is a preset distance threshold.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the star image selection method for evaluating astronomical images from space telescopes as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which is loaded and executed by a processor to enable the computer to implement the star image selection method for evaluating astronomical images from a space telescope as described in any one of claims 1-5.

Images