Method, device, equipment and medium for extracting faint and elongated star images

Abstract

The present invention provides a method for extracting a faint and elongated star image, which can be applied to the technical field of space object detection, including: preprocessing an observed image to obtain a preprocessed image; extracting bright star image information, and calculating motion parameters and point spread function parameters of the bright star image; constructing a Gabor convolution kernel function based on the motion parameters and point spread function parameters; performing convolution processing on the original observed image using the Gabor convolution kernel function to obtain a convolution filter image; performing binarization processing on the convolution filter image to obtain a binarized image; fusing the original observed image and the binarized image to obtain a fused image; and extracting a faint and elongated star image and the center position and flow of the star image based on the fused image. Through convolution processing, faint target information is mined from the image, and fusion processing is performed in combination with the original image to achieve high-precision extraction of faint star image information. The present invention also provides a faint and elongated star image extraction device, equipment and medium.

Description

Technical Field

[0001] The present invention relates to the technical field of space object detection, and in particular to a method, device, equipment and medium for extracting faint and elongated star images. Background Art

[0002] When tracking and observing low-orbit targets, the star images on the image are elongated and dim due to factors such as the fast target movement speed and bright sky background. When the target is extracted by traditional threshold segmentation methods, it often results in a small number of extracted star images, low position accuracy, large flow errors, etc., which makes it difficult to meet the data accuracy requirements of subsequent applications. Specifically, for low-orbit target tracking images, the centroid extraction is difficult or the error is large due to the fast target speed and the elongated and dim star images, resulting in low astronomical positioning accuracy or even unsuccessful positioning, which cannot meet the needs of subsequent applications. Therefore, research on the extraction method of dim and elongated star images is crucial for tracking and observing low-orbit targets. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In order to solve the problem in the prior art that, during low-orbit target tracking and observation, due to the influence of factors such as the fast target movement speed and the bright sky background, the star image on the image is elongated and dim, and it is difficult to accurately extract the position and flow information of the star image, the embodiments of the present invention provide a method, device, equipment and medium for extracting dim and elongated star images. According to the basic law that the imaging of star images in the detector has the same direction and length during the tracking and observation process, a Gabor convolution kernel function is constructed based on the information of bright star images in the image, more dim target information is mined from the image through convolution processing, and fusion processing is performed with the original image, thereby achieving high-precision extraction of dim star image information.

[0005] (II) Technical solution

[0006] In view of the above technical problems, embodiments of the present invention provide a method, device, equipment and medium for extracting faint and elongated star images.

[0007] According to a first aspect of the present invention, a method for extracting faint and elongated star images is provided, comprising: acquiring an original observed image; preprocessing the original observed image to obtain a preprocessed observed image; extracting bright star image information based on the preprocessed observed image, and calculating motion parameters and point spread function parameters of the bright star image; constructing a Gabor convolution kernel function based on the motion parameters and the point spread function parameters; performing convolution processing on the original observed image using the Gabor convolution kernel function to obtain a convolution filter image; performing binarization processing on the convolution filter image using a threshold segmentation method to obtain a binarized image; performing information fusion processing on the original observed image and the binarized image to obtain a fused processed image; and extracting faint and elongated star images and the center position and flow of the star images based on the fused processed image.

[0008] In some exemplary embodiments, preprocessing the original observed image includes calculating and deducting a background value of the original observed image.

[0009] In some exemplary embodiments, based on the preprocessed observation image, extracting bright star image information, and calculating motion parameters and point spread function parameters of the bright star image include: extracting bright star image information in the preprocessed observation image; based on the bright star image information, calculating motion parameters of the bright star image relative to the camera, wherein the motion parameters include the star image motion direction angle θ 0 And the movement distance L during the exposure time; based on the bright star image information, the point spread function parameters are fitted to obtain the standard deviation σ of the Gaussian factor.

[0010] In some exemplary embodiments, the formula for fitting the point spread function parameters is:

[0011] PSF(x,y) = A×exp[- ((xx 0 ) 2 + (yy 0 ) 2 ) / (2σ 2 )]

[0012] Among them, PSF(x, y) is the energy of the point source at coordinates (x, y), x 0 and 0 represents the central pixel coordinate value of the point source, σ is the standard deviation of the Gaussian factor in the function, and A is the amplitude of the point source signal.

[0013] In some exemplary embodiments, a convolution filter image is binarized using a threshold segmentation method to obtain a binary image, including: calculating a background image of the convolution filter image, and generating a convolution image after subtracting the background image; and binarizing the convolution image after subtracting the background image using a threshold segmentation method to obtain a binary image, wherein the binary image contains the basic outline of a dark and elongated star image.

[0014] In some exemplary embodiments, a Gabor convolution kernel function is constructed based on motion parameters and point spread function parameters, including using the real part of a Gabor filter to construct a Gabor convolution kernel function for extracting dim targets in a space object detection image.

[0015] In some exemplary embodiments, the Gabor convolution kernel function G(x, y, ω) at the coordinate (x, y) n , θ m ,φ,σ) is:

[0016] in, , θ m Indicates the direction of the parallel stripes of the Gabor convolution kernel function, ranging from 0 to 360 degrees; for images that can extract bright stars: ,in, is the angle of the star image motion direction; for images that cannot produce bright stars: ;ω n represents the frequency of the Gabor filter and is calculated as follows: ; σ represents the standard deviation of the Gaussian factor in the function, which is obtained by fitting the point spread function parameters; φ represents the phase shift, and its value range is -π to π.

[0017] According to a second aspect of the present invention, a device for extracting faint and elongated star images is provided, comprising: an acquisition module for acquiring an original observation image; a preprocessing module for preprocessing the original observation image to obtain a preprocessed observation image; a calculation module for extracting bright star image information based on the preprocessed observation image, and calculating motion parameters and point spread function parameters of the bright star image; a construction module for constructing a Gabor convolution kernel function based on the motion parameters and the point spread function parameters; a first processing module for convolving the original observation image using the Gabor convolution kernel function to obtain a convolution filter image; a second processing module for binarizing the convolution filter image using a threshold segmentation method to obtain a binarized image; a third processing module for performing information fusion processing on the original observation image and the binarized image to obtain a fused processed image; and an extraction module for extracting faint and elongated star images and the center position and flow of the star images based on the fused processed image.

[0018] According to a third aspect of the present invention, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is executed by the processor to implement the above method.

[0019] According to a fourth aspect of the present invention, a storage medium is provided, characterized in that: computer-readable instructions are stored in the storage medium, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the above method.

[0020] (III) Beneficial effects

[0021] It can be seen from the above technical solutions that the method, device, equipment and medium for extracting faint and elongated star images provided by the embodiments of the present invention have at least the following beneficial effects:

[0022] (1) First, the characteristic information of bright star targets in the image is extracted, and then the Gabor convolution kernel function is constructed using this characteristic information to perform convolution processing on the original image. More information about dim targets can be extracted from the convolved image. Compared with the traditional threshold segmentation method, the success rate and accuracy of extracting dim and elongated star images are greatly improved.

[0023] (2) It can effectively extract dim and elongated star images with low signal-to-noise ratio in starry sky images. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0025] Figure 1 The flowchart of a method for extracting faint and elongated star images according to an embodiment of the present invention is schematically shown.

[0026] Figure 2 A logic diagram of a method for extracting faint and elongated star images according to an embodiment of the present invention is schematically shown.

[0027] Figure 3 The flowchart of extracting bright star image information based on preprocessed observed images and calculating motion parameters and point spread function parameters of bright star images according to an embodiment of the present invention is schematically shown.

[0028] Figure 4 The flowchart of using the threshold segmentation method to binarize a convolution filter image to obtain a binarized image according to an embodiment of the present invention is schematically shown.

[0029] Figure 5 The figure schematically shows an original observation image of a satellite taken by a telescope, with an exposure time of 0.15 seconds.

[0030] Figure 6 The figure schematically shows an image of a satellite taken by a telescope after being processed based on Gabor convolution filtering.

[0031] Figure 7The structure block diagram of a device for extracting faint and elongated star images according to an embodiment of the present invention is schematically shown.

[0032] Figure 8 A block diagram of an electronic device for extracting a faint and elongated star image according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Figure 1 A schematic diagram of a process of extracting a faint and elongated star image according to an embodiment of the present invention is shown; Figure 2 A logic diagram of a method for extracting faint and elongated star images according to an embodiment of the present invention is schematically shown.

[0035] Depend on Figure 1 It can be seen that a method for extracting faint and elongated star images according to an embodiment of the present invention includes steps S110-S180.

[0036] In step S110 , an original observation image is acquired.

[0037] In step S120, the original observation image is preprocessed to obtain a preprocessed observation image. Optionally, preprocessing the original observation image includes calculating and deducting a background value of the original observation image.

[0038] In step S130, based on the preprocessed observation image, the bright star image information is extracted, and the motion parameters and point spread function (PSF) parameters of the bright star image are calculated. Among them, PSF is a key parameter that describes the response of the optical system to a point light source. When a beam of light is emitted from a point source and passes through an optical system (such as a lens, eye, etc.), it should theoretically remain as a point. However, due to factors such as diffraction, scattering, and aberration, this point will spread into a light spot. PSF is a mathematical description of the shape and intensity of this light spot.

[0039] In some exemplary embodiments, step S130 includes steps S131-S133, see Figure 3 .

[0040] In step S131, bright star image information in the preprocessed observation image is extracted.

[0041] In step S132, based on the bright star image information, the motion parameters of the bright star image relative to the camera are calculated, wherein the motion parameters include the star image motion direction angle θ 0 and the movement distance L during the exposure time.

[0042] In step S133, based on the bright star image information, the point spread function parameters are fitted to obtain the standard deviation σ of the Gaussian factor.

[0043] For example, the formula for fitting the point spread function parameters is:

[0044] PSF(x,y) = A×exp[- ((xx 0 ) 2 + (yy 0 ) 2 ) / (2σ 2 )] (1)

[0045] Among them, PSF(x, y) is the energy of the point source at coordinates (x, y), x 0 and 0 represents the central pixel coordinate value of the point source, σ is the standard deviation of the Gaussian factor in the function, and A is the amplitude of the point source signal.

[0046] In step S140, a Gabor convolution kernel function is constructed based on motion parameters and point spread function parameters. Among them, the Gabor convolution kernel function can extract relevant features at different scales and directions in the frequency domain. In addition, the Gabor convolution kernel function is similar to the biological function of the human eye, so it is often used for texture recognition and has achieved good results. The Gabor convolution kernel function is a filter with directionality and frequency selectivity, which can enhance or suppress image information in a specific direction and frequency by adjusting parameters. Its essence is a two-dimensional filter in complex form, which contains the product of a sine wave (frequency component) and a Gaussian function (spatial localization). The sine wave determines the sensitivity of the kernel function to a specific frequency, while the Gaussian function ensures locality, enabling it to capture the features of the local area.

[0047] Preferably, the real part of the Gabor filter is used to construct a Gabor convolution kernel function for extracting dim targets in space object detection images. The Gabor filter is a linear filter used for image processing, which is inspired by the receptive field model in the biological visual system. It can extract features such as direction, frequency and spatial locality in the image, so it is widely used in texture classification, edge detection, face recognition and other fields. The basic principle of the Gabor filter is to convolve the image with the Gabor function to extract the texture features of the image.

[0048] The Gabor convolution kernel function G(x,y, ω) at coordinate (x,y)n , θ m ,φ,σ) is:

[0049] (2)

[0050] in, ; θ m Indicates the direction of the parallel stripes of the Gabor convolution kernel function, ranging from 0 to 360 degrees.

[0051] For images that can extract bright stars:

[0052] (3)

[0053] in, is the angle of the star's motion direction.

[0054] For images that cannot reveal bright stars:

[0055] (4)

[0056] ω n represents the frequency of the Gabor filter and is calculated as follows:

[0057] (5)

[0058] σ represents the standard deviation of the Gaussian factor within the function, which is obtained by fitting the PSF parameters.

[0059] φ represents the phase shift, and its value range is -π to π.

[0060] In step S150, the original observed image is convolved using a Gabor convolution kernel function to obtain a convolution filtered image.

[0061] In step S160, the convolution filter image is binarized using a threshold segmentation method to obtain a binarized image.

[0062] In some exemplary embodiments, step S160 includes steps S161-S162, see Figure 4 .

[0063] In step S161, the background image of the convolution filter image is calculated, and a convolution image is generated after the background image is subtracted.

[0064] In step S162, the convolution image after the background image is subtracted is binarized using a threshold segmentation method to obtain a binarized image, wherein the binarized image contains the basic outline of the dark and elongated star image.

[0065] In step S170, information fusion processing is performed on the original observed image and the binary image to obtain a fused image.

[0066] In step S180, based on the fused processed image, the faint and elongated star image and the center position and flow rate of the star image are extracted.

[0067] Figure 5 The figure schematically shows an original observation image of a satellite taken by a telescope, with an exposure time of 0.15 seconds.

[0068] Depend on Figure 5 It can be seen that since the observation time was close to the early morning, there were few bright stars in the original observation image, making it difficult to achieve accurate astronomical positioning.

[0069] Figure 6 The figure schematically shows an image of a satellite taken by a telescope after being processed based on Gabor convolution filtering.

[0070] Depend on Figure 6 It can be seen that Figure 5 compared to, Figure 6 The number of bright stars in the images has increased significantly, greatly improving the success rate of astronomical positioning.

[0071] The embodiment of the present invention proposes a method for extracting faint elongated star images based on a Gabor filter. First, the characteristic information of bright star targets in the image is extracted, and then the Gabor convolution kernel function is constructed using the characteristic information to perform convolution processing on the original image. More faint target information can be extracted from the convolved image, which greatly improves the success rate and accuracy of extracting faint elongated star images compared to the traditional threshold segmentation method. Experiments have shown that the Gabor filter-based method for extracting faint elongated star images has a significant improvement in the success rate of astronomical positioning after target extraction compared to the traditional threshold segmentation target extraction method. The results of telescope-based observation data processing show that the astronomical positioning success rate after processing by this method is improved by more than 10% compared to the traditional method, which greatly improves the success rate of observation data processing.

[0072] Figure 7 The structure block diagram of a device for extracting faint and elongated star images according to an embodiment of the present invention is schematically shown.

[0073] like Figure 7 As shown, a faint and elongated star image extraction device 800 according to an embodiment of the present invention includes an acquisition module 810, a preprocessing module 820, a calculation module 830, a construction module 840, a first processing module 850, a second processing module 860, a third processing module 870 and an extraction module 880.

[0074] The acquisition module 810 is used to acquire the original observed image.

[0075] The preprocessing module 820 is used to preprocess the original observation image to obtain a preprocessed observation image.

[0076] The calculation module 830 is used to extract the bright star image information based on the preprocessed observation image, and calculate the motion parameters and point spread function parameters of the bright star image.

[0077] The construction module 840 is used to construct a Gabor convolution kernel function based on the motion parameters and the point spread function parameters.

[0078] The first processing module 850 is used to perform convolution processing on the original observation image using the Gabor convolution kernel function to obtain a convolution filtered image.

[0079] The second processing module 860 is used to perform binarization processing on the convolution filter image by using a threshold segmentation method to obtain a binarized image.

[0080] The third processing module 870 is used to perform information fusion processing on the original observed image and the binary image to obtain a fused processed image.

[0081] The extraction module 880 is used to extract the faint and elongated star image and the center position and flow of the star image based on the fusion processing image.

[0082] In some specific embodiments, any multiple modules among the acquisition module 810, the preprocessing module 820, the calculation module 830, the construction module 840, the first processing module 850, the second processing module 860, the third processing module 870 and the extraction module 880 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0083] In some specific embodiments, at least one of the acquisition module 810, the preprocessing module 820, the calculation module 830, the construction module 840, the first processing module 850, the second processing module 860, the third processing module 870 and the extraction module 880 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the acquisition module 810, the preprocessing module 820, the calculation module 830, the construction module 840, the first processing module 850, the second processing module 860, the third processing module 870 and the extraction module 880 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0084] Figure 8 A block diagram of an electronic device for extracting a faint and elongated star image according to an embodiment of the present invention is schematically shown.

[0085] like Figure 8 As shown, the electronic device 600 according to an embodiment of the present invention includes a processor 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 to the random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an on-board memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0086] In RAM603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM602 and RAM603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to an embodiment of the present invention by executing the program in ROM602 and / or RAM603. It should be noted that the program can also be stored in one or more memories other than ROM602 and RAM603. Processor 601 can also perform various operations of the method flow according to an embodiment of the present invention by executing the program stored in one or more memories.

[0087] In some specific embodiments, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0088] An embodiment of the present invention shows a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0089] In some specific embodiments, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0090] An embodiment of the present invention shows a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiment of the present invention.

[0091] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when it is executed by the processor 601. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0092] In some specific embodiments, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0093] In some specific embodiments, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[0094] In some specific embodiments, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages, specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, such as Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0095] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0096] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for extracting faint and elongated star images, characterized in that: include: Obtaining the original observation image; Preprocessing the original observation image to obtain a preprocessed observation image; Based on the preprocessed observed image, extracting bright star image information, and calculating motion parameters and point spread function parameters of the bright star image; constructing a Gabor convolution kernel function based on the motion parameters and the point spread function parameters; Performing convolution processing on the original observed image using the Gabor convolution kernel function to obtain a convolution filtered image; Performing binarization processing on the convolution filter image by using a threshold segmentation method to obtain a binarized image; Performing information fusion processing on the original observed image and the binary image to obtain a fused processed image; as well as Based on the fused processed image, extract the faint elongated star image and the center position and flow rate of the star image, The step of extracting bright star image information based on the preprocessed observed image and calculating motion parameters and point spread function parameters of the bright star image includes: Extracting bright star image information from the preprocessed observation image; Based on the bright star image information, calculate the motion parameters of the bright star image relative to the camera, where the motion parameters include the star image motion direction angle θ0 and the motion distance L within the exposure time; and Based on the bright star image information, point spread function parameters are fitted to obtain the standard deviation σ of the Gaussian factor.

2. The method according to claim 1, characterized in that Preprocessing the original observed image includes calculating and deducting a background value of the original observed image.

3. The method according to claim 1, characterized in that The formula for the fitting point spread function parameters is: PSF(x,y) = A×exp[- ((x-x0) 2 + (y-y0) 2 ) / (2σ 2 )] Among them, PSF(x, y) is the energy of the point source at the coordinate (x, y), x0 and y0 represent the central pixel coordinate values ​​of the point source, σ is the standard deviation of the Gaussian factor in the function, and A is the amplitude of the point source signal.

4. The method according to claim 1, characterized in that: The method of performing binarization processing on the convolution filter image by using a threshold segmentation method to obtain a binarized image includes: Calculating a background image of the convolution filter image and generating a convolution image after deducting the background image; The convolution image after deducting the background image is binarized by using a threshold segmentation method to obtain a binarized image. The binary image contains the basic outline of the faint and elongated star image.

5. The method according to claim 1, characterized in that The method of constructing a Gabor convolution kernel function based on the motion parameters and the point spread function parameters includes using the real part of a Gabor filter to construct a Gabor convolution kernel function for extracting dark targets in a space object detection image.

6. The method according to claim 5, characterized in that The Gabor convolution kernel function G(x,y,ω) at coordinate (x,y) n , θ m ,φ,σ) is: ， in, , ; θ m Indicates the direction of the parallel stripes of the Gabor convolution kernel function, ranging from 0 to 360 degrees; For images that can extract bright stars, ; in, is the angle of the star image's motion direction; For images that cannot reveal bright stars, ; ω n represents the frequency of the Gabor filter and is calculated as follows: ； σ represents the standard deviation of the Gaussian factor within the function, which is obtained by fitting the point spread function parameters; φ represents the phase shift, and its value range is -π to π.

7. A device for extracting faint and elongated star images, characterized in that: The device comprises: An acquisition module, used for acquiring original observation images; A preprocessing module, used for preprocessing the original observation image to obtain a preprocessed observation image; A calculation module, used for extracting bright star image information based on the preprocessed observation image, and calculating motion parameters and point spread function parameters of the bright star image; A construction module, used for constructing a Gabor convolution kernel function based on the motion parameters and the point spread function parameters; A first processing module, used for performing convolution processing on the original observation image using the Gabor convolution kernel function to obtain a convolution filter image; A second processing module is used to perform binarization processing on the convolution filter image by using a threshold segmentation method to obtain a binarized image; A third processing module is used to perform information fusion processing on the original observed image and the binary image to obtain a fused image; and An extraction module is used to extract a faint and elongated star image and the center position and flow rate of the star image based on the fusion processed image. The step of extracting bright star image information based on the preprocessed observed image and calculating motion parameters and point spread function parameters of the bright star image includes: Extracting bright star image information from the preprocessed observation image; Based on the bright star image information, calculate the motion parameters of the bright star image relative to the camera, where the motion parameters include the star image motion direction angle θ0 and the motion distance L within the exposure time; and Based on the bright star image information, point spread function parameters are fitted to obtain the standard deviation σ of the Gaussian factor.

8. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the method according to any one of claims 1 to 6.

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