Undersampled image processing method, device and equipment of astronomical telescope and medium

Abstract

The invention discloses an astronomical telescope under-sampling image processing method, device and equipment and a medium, and relates to the technical field of optical testing, and the method comprises the steps: collecting an under-sampling image through employing a collection number and a micrometric displacement mode which are determined based on a target pixel size and an initial pixel size of the under-sampling image; determining a size search interval, a search step size and a current virtual distribution unit size based on the target pixel size, and splicing an image with the current virtual distribution unit size by adopting a Drizzle algorithm to obtain a current reconstructed image; transforming the current reconstructed image to calculate a high-frequency noise power function term and a power spectrum entropy function term and obtain a current function value; and if the size of the current virtual distribution unit is located in the search interval, updating the size of the current virtual distribution unit, skipping to the step of determining the current reconstructed image until the size of the current virtual distribution unit is not located in the search interval, and selecting the reconstructed image with the minimum function value as a target image. Therefore, the image processing efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of optical testing technology, and in particular to an under-sampling image processing method, device, equipment and medium for an astronomical telescope. Background Art

[0002] Currently, the point spread function (PSF) is a core element in evaluating the imaging quality of astronomical telescopes and a fundamental element of modern astrophysics research. The accuracy of PSF measurements not only directly impacts the quality of cutting-edge work such as on-orbit evaluation of telescope imaging quality, on-orbit active optical adjustment, and on-orbit assembly, but also indirectly influences scientific output in cutting-edge fields such as weak gravitational lensing cosmology and direct imaging of exoplanets. However, the mismatch between the diffraction limit of the optical system and the detector pixel size leads to PSF undersampling, resulting in loss of high-frequency information in the core region of the PSF and spectral aliasing. This, in turn, leads to distortion of the PSF morphological distribution and reduced accuracy in full-width at half-maximum and ellipticity measurements. This problem is exacerbated by the continued increase in the aperture of astronomical telescopes and the severe limitations of semiconductor detector manufacturing processes.

[0003] The existing methods for dealing with the PSF undersampling problem of astronomical telescopes are mainly as follows:

[0004] The linear interpolation algorithm is based on a specific mathematical interpolation model and uses the grayscale information of the surrounding integer pixel positions to interpolate and calculate the grayscale information of the sub-pixel position, wherein the mathematical interpolation model includes a bicubic interpolation model and a spline interpolation model.

[0005] A multi-star image registration method is proposed. This method reconstructs a high-resolution star image using multiple undersampled star images with sub-pixel micro-displacements. The main steps are as follows: first, the telescope takes multiple undersampled star images, and sub-pixel micro-displacements are maintained between each image; second, an undersampled image coordinate system is constructed, and the coordinates of the centroids of all undersampled star images in the undersampled image coordinate system are calculated; third, a high-resolution image coordinate system is constructed, and the coordinates of the center position of the high-resolution image are used as the registration point of the centroids of all undersampled star images, and the coordinate transformation relationship between all undersampled images and the high-resolution image is calculated; then, the undersampled image pixels are used as the allocation unit of the registration process, and the range of high-resolution image pixels covered by each undersampled image pixel is calculated; finally, the grayscale information of each undersampled image pixel is evenly or according to a specific weight coefficient distributed to the high-resolution image pixels it covers, to obtain a reconstructed high-resolution star image.

[0006] The traditional Drizzle algorithm reconstructs a high-resolution star image using multiple undersampled star images with sub-pixel micro-displacements. The main difference from the multi-star image registration method is that the traditional Drizzle algorithm does not use the undersampled image pixels as the allocation unit in the registration process, but instead creates a smaller allocation unit to increase the retention of high-frequency information during the reconstruction process. The size of the virtual allocation unit is manually assigned based on experience, usually 0.5 to 0.8 of the undersampled image pixel size. The main steps of the traditional Drizzle algorithm are as follows: first, multiple undersampled star point images are captured through a telescope, and sub-pixel micro-displacements are maintained between each image; second, an undersampled image coordinate system is constructed, and the coordinates of the centroids of all undersampled star point images in the undersampled image coordinate system are calculated; third, a high-resolution image coordinate system is constructed, with the coordinates of the center position of the high-resolution image as the registration point for the centroids of all undersampled star point images, and the coordinate transformation relationship between all undersampled images and the high-resolution image is calculated; then, a virtual allocation unit is constructed, and the range of high-resolution image pixels covered by each undersampled image pixel is calculated according to the size of the virtual allocation unit; finally, the grayscale information of each undersampled image pixel is distributed to the high-resolution image pixels it covers according to a specific weight coefficient to obtain a reconstructed high-resolution star point image.

[0007] From the above, we can see that the existing methods for dealing with the PSF undersampling problem of astronomical telescopes have the following limitations: Linear interpolation algorithm: The essence of this algorithm is mathematical interpolation rather than physical information reconstruction, and it cannot restore the true diffraction-limited high-frequency information. Multi-star image registration method: This method uses undersampled image pixels as the allocation unit of the registration process. The allocation unit acts as a low-pass filter in the frequency domain, and will actively suppress the real signals in the higher frequency bands during the reconstruction process; the size of the allocation unit of the multi-star image registration method is equal to the size of the undersampled image pixel, so this method will suppress the real signals with frequencies higher than the corresponding frequency of the undersampled image pixel size. Therefore, this method is difficult to effectively supplement the high-frequency information lost in the undersampled image, and can only improve the measurement accuracy of the low-frequency morphology of the PSF. Traditional Drizzle algorithm: This method is an improvement on the multi-star image registration method. Since a virtual allocation unit is used, and the size setting of the virtual allocation unit is relatively flexible, this method can theoretically better supplement the high-frequency information lost in the undersampled image. In theory, a smaller virtual allocation unit size improves the recovery of high-frequency information. However, preserving high-frequency information and suppressing high-frequency aliasing are often difficult to reconcile, and finding the optimal balance between the two is a core concern of all reconstruction algorithms. The traditional Drizzle algorithm uses an empirically assigned virtual allocation unit size, typically between 0.5 and 0.8 of the undersampled image pixel size. This assignment method lacks theoretical basis, making it difficult to find the optimal balance between preserving high-frequency information and suppressing high-frequency aliasing.

[0008] As can be seen from the above, how to improve the efficiency of image processing in the undersampled image processing process of astronomical telescopes is a problem that needs to be solved urgently. Summary of the Invention

[0009] In view of this, the present invention aims to provide a method, apparatus, device, and medium for processing undersampled images of astronomical telescopes, which can improve the efficiency of image processing during undersampled image processing of astronomical telescopes. The specific scheme is as follows:

[0010] In a first aspect, the present application provides a method for processing undersampled images of an astronomical telescope, comprising:

[0011] Determining a target pixel size based on preset requirements, and determining the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and an initial pixel size corresponding to the undersampled image, and then collecting the undersampled image output by the astronomical telescope based on the number to be collected and the micro-displacement method; the target pixel size is smaller than the initial pixel size;

[0012] determining a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, so as to stitch the undersampled images together using a Drizzle algorithm in the size search interval according to the search step and based on the current virtual allocation unit size to obtain a current reconstructed image;

[0013] Determining a Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determining a high-frequency noise power function term and a power spectrum entropy function term based on the Fourier transform result, and determining a current function value based on the high-frequency noise power function term and the power spectrum entropy function term;

[0014] Determine whether the current virtual allocation unit size is within the size search interval; if so, update the current virtual allocation unit size to obtain a new current virtual allocation unit size, and jump back to the step of stitching the undersampled images according to the search step size in the size search interval based on the current virtual allocation unit size and using the Drizzle algorithm to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval, and then set the current reconstructed image corresponding to the function value with the smallest function value among the current function values ​​as the target image.

[0015] Optional,

[0016] The method includes determining a target pixel size based on a preset requirement, determining the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and an initial pixel size corresponding to the undersampled image, and then collecting the undersampled image output by the astronomical telescope based on the number of undersampled images to be collected and the micro-displacement method, including:

[0017] determining an image to be processed corresponding to the astronomical telescope, and determining whether an image resolution corresponding to the image to be processed meets a preset image resolution condition; if the image resolution corresponding to the image to be processed does not meet the preset image resolution condition, setting the image to be processed as an undersampled image;

[0018] Determining a target pixel size based on preset requirements and determining an initial pixel size corresponding to the undersampled image, determining a size ratio based on the target pixel size and the initial pixel size, and then determining a number of pixels to be collected and a micro-displacement method corresponding to the undersampled image based on the size ratio;

[0019] A preset jittering method is used and based on the number to be collected and the micro-displacement method, several frames of under-sampled images with sub-pixel displacement output by the astronomical telescope are collected.

[0020] Optionally, determining a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, and stitching the undersampled images according to the search step in the size search interval based on the current virtual allocation unit size and using a Drizzle algorithm to obtain a current reconstructed image, includes:

[0021] Determining a preset lower limit coefficient and a preset upper limit coefficient based on preset requirements, determining a lower limit value of a size search interval based on the target pixel size corresponding to the undersampled image and the preset lower limit coefficient, and then determining an upper limit value of the size search interval based on the target pixel size corresponding to the undersampled image and the preset upper limit coefficient;

[0022] Determining a size search interval of a resampling grid based on the size search interval lower limit value and the size search interval upper limit value, and determining a search step size and a current virtual allocation unit size corresponding to the resampling grid based on the target pixel size;

[0023] The undersampled images are processed sequentially in the size search interval according to the search step size using a Drizzle algorithm and based on the current virtual allocation unit size to obtain a current reconstructed image, and the current reconstructed image is processed using a preset Fourier transform algorithm to obtain a Fourier transform result.

[0024] Optionally, determining a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size includes:

[0025] An arithmetic mean of a lower limit value of the size search interval and an upper limit value of the size search interval is determined, and a current virtual allocation unit size is determined based on the arithmetic mean value and a pixel size of the undersampled image. Then, a search step size is determined based on a preset accuracy requirement and an initial pixel size corresponding to the undersampled image.

[0026] Optionally, determining a Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determining a high-frequency noise power function term and a power spectrum entropy function term based on the Fourier transform result, and determining a current function value based on the high-frequency noise power function term and the power spectrum entropy function term, includes:

[0027] performing a two-dimensional Fourier transform on the current reconstructed image based on the current virtual allocation unit size to obtain a frequency domain response function corresponding to the current reconstructed image, determining a power spectrum entropy function and a high-frequency noise power function corresponding to the current reconstructed image based on the frequency domain response function, and then determining a resolvable frequency threshold based on the target pixel size and the initial pixel size;

[0028] Determining a high-frequency noise power function term and a power spectrum entropy function term based on the resolvable frequency threshold using the power spectrum entropy function and the high-frequency noise power function;

[0029] Based on preset requirements, a first weight coefficient corresponding to the high-frequency noise power function item and a second weight coefficient corresponding to the power spectrum entropy function item are determined to determine the current function value based on the first weight coefficient, the second weight coefficient, the high-frequency noise power function item and the power spectrum entropy function item.

[0030] Optionally, determining the current function value based on the high-frequency noise power function term and the power spectrum entropy function term includes:

[0031] Determining a degree of clarity improvement based on a first power spectrum entropy function item corresponding to the current reconstructed image and a second power spectrum entropy function item corresponding to the undersampled image, and determining a degree of cleanliness improvement based on a first high-frequency noise power function item corresponding to the current reconstructed image and a second high-frequency noise power function item corresponding to the undersampled image;

[0032] Determine a first weight coefficient corresponding to the degree of clarity improvement, and determine a second weight coefficient based on the degree of cleanliness improvement, so as to determine a clarity score based on the degree of clarity improvement and the first weight coefficient, and determine a cleanliness score based on the degree of cleanliness improvement and the second weight coefficient, and then determine a corresponding current function value based on each of the clarity scores and each of the cleanliness scores.

[0033] Optionally, the step of determining whether the current virtual allocation unit size is within the size search interval, and if so, updating the current virtual allocation unit size to obtain a new current virtual allocation unit size, and re-jumping to the step of stitching the undersampled images according to the search step size in the size search interval using the Drizzle algorithm to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval, includes:

[0034] determining whether the current virtual allocation unit size is within the interval length corresponding to the size search interval; if the current virtual allocation unit size is within the interval length corresponding to the size search interval, increasing the current virtual allocation unit size according to the search step to obtain a new current virtual allocation unit size, and re-jumping to the step of stitching the undersampled images in the size search interval according to the search step based on the current virtual allocation unit size using the Drizzle algorithm to obtain a current reconstructed image;

[0035] If the current virtual allocation unit size is not within the interval length corresponding to the size search interval, the process jumps to the step of setting the current reconstructed image corresponding to the smallest function value among the current function values ​​as the target image.

[0036] In a second aspect, the present application provides an undersampling image processing device for an astronomical telescope, comprising:

[0037] an undersampled image determination module, configured to determine a target pixel size based on preset requirements, determine the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and an initial pixel size corresponding to the undersampled image, and then collect the undersampled image output by the astronomical telescope based on the number of undersampled images to be collected and the micro-displacement method; the target pixel size is smaller than the initial pixel size;

[0038] a reconstructed image determination module, configured to determine a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, so as to combine the undersampled images in the size search interval according to the search step and using a Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image;

[0039] a function value determination module, configured to determine a Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determine a high-frequency noise power function term and a power spectrum entropy function term based on the Fourier transform result, and determine a current function value based on the high-frequency noise power function term and the power spectrum entropy function term;

[0040] a target image determination module, configured to determine whether the current virtual allocation unit size is within the size search interval; if so, to update the current virtual allocation unit size to obtain a new current virtual allocation unit size; and to jump back to the step of stitching the undersampled images within the size search interval according to the search step size and using the Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval; and then setting the current reconstructed image corresponding to the smallest function value among the current function values ​​as the target image.

[0041] In a third aspect, the present application provides an electronic device, comprising:

[0042] Memory, used to store computer programs;

[0043] The processor is used to execute the computer program to implement the above-mentioned under-sampling image processing method for the astronomical telescope.

[0044] In a fourth aspect, the present application provides a computer-readable medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned undersampling image processing method for an astronomical telescope.

[0045] As can be seen from the above, before the present application performs under-sampled image processing of an astronomical telescope, it is necessary to determine the target pixel size based on a preset requirement, and determine the number of under-sampled images to be collected and the micro-displacement method based on the target pixel size and the initial pixel size corresponding to the under-sampled image, and then collect the under-sampled image output by the astronomical telescope based on the number to be collected and the micro-displacement method; the target pixel size is smaller than the initial pixel size; the size search interval, the search step and the current virtual allocation unit size are determined based on the target pixel size, so as to combine the under-sampled images according to the search step in the size search interval based on the current virtual allocation unit size and using the Drizzle algorithm to obtain the current reconstructed image; the size of the under-sampled image corresponding to the current reconstructed image is determined based on the current virtual allocation unit size. The Fourier transform result of the image processing unit is used to determine the high-frequency noise power function term and the power spectrum entropy function term based on the Fourier transform result, and the current function value is determined based on the high-frequency noise power function term and the power spectrum entropy function term; it is judged whether the current virtual allocation unit size is within the size search interval, and if so, the current virtual allocation unit size is updated to obtain a new current virtual allocation unit size, and the process jumps back to the step of stitching the undersampled images according to the search step size and using the Drizzle algorithm in the size search interval based on the current virtual allocation unit size to obtain the current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval, and then the current reconstructed image corresponding to the function value with the smallest function value among the current function values ​​is set as the target image.

[0046] It can be seen that the present application first needs to determine the target pixel size based on the preset requirements, and determine the number of under-sampled images to be collected and the micro-displacement method based on the target pixel size and the initial pixel size corresponding to the under-sampled image, and then collect the under-sampled image output by the astronomical telescope based on the number to be collected and the micro-displacement method; secondly, determine the size search interval, search step and current virtual allocation unit size based on the target pixel size, and use the Drizzle algorithm to stitch the under-sampled images in the size search interval according to the search step based on the current virtual allocation unit size to obtain the current reconstructed image; then, determine the Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, and use the Fourier transform algorithm to obtain the current reconstructed image based on the Fourier transform algorithm. The transformation results determine the high-frequency noise power function term and the power spectrum entropy function term, and the current function value is determined based on the high-frequency noise power function term and the power spectrum entropy function term. Finally, it is determined whether the current virtual allocation unit size is within the size search interval. If so, the current virtual allocation unit size is updated to obtain a new current virtual allocation unit size. The process then jumps back to the step of stitching the undersampled images in the size search interval using the Drizzle algorithm according to the search step size based on the current virtual allocation unit size to obtain the current reconstructed image. This process continues until the current virtual allocation unit size is no longer within the size search interval. The current reconstructed image corresponding to the function value with the smallest function value among the current function values ​​is then set as the target image. In this way, the efficiency of processing undersampled images in astronomical telescopes is improved, thereby enhancing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0048] Figure 1 This is a flow chart of an under-sampling image processing method for an astronomical telescope disclosed in this application;

[0049] Figure 2 This is a schematic structural diagram of an undersampling image processing device for an astronomical telescope disclosed in this application;

[0050] Figure 3 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0052] At present, the point spread function is the core element of astronomical telescope imaging quality evaluation and the core foundation of modern astrophysics research. The PSF measurement accuracy can not only directly affect the quality of cutting-edge work such as on-orbit evaluation of astronomical telescope imaging quality, on-orbit active optical adjustment and on-orbit assembly, but also indirectly affect the scientific output of cutting-edge fields such as weak gravitational lensing cosmology and direct imaging of exoplanets. However, the mismatch between the diffraction limit of the optical system and the pixel size of the detector leads to the problem of PSF undersampling, resulting in the loss of high-frequency information in the core area of ​​the PSF and spectrum aliasing, which in turn leads to the distortion of the PSF morphological distribution, the decrease in the half-maximum full width and ellipticity measurement accuracy, etc. As the aperture of astronomical telescopes continues to increase and the manufacturing process of semiconductor detectors is severely limited, this problem is becoming increasingly serious. To this end, the present application provides an under-sampling image processing method for astronomical telescopes, which can improve the efficiency of image processing during the under-sampling image processing of astronomical telescopes.

[0053] See also Figure 1 As shown, an embodiment of the present invention discloses a method for processing under-sampling images of an astronomical telescope, comprising:

[0054] Step S11: determining a target pixel size based on preset requirements, and determining the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and the initial pixel size corresponding to the undersampled image, and then collecting the undersampled image output by the astronomical telescope based on the number to be collected and the micro-displacement method; the target pixel size is smaller than the initial pixel size.

[0055] In this embodiment, the embodiment of the present application first needs to determine multiple images with sub-pixel displacement based on the undersampled image output by the astronomical telescope through dithering (i.e. dithering processing), and then determine the pixel size of the reconstructed image according to the application requirements. , and the pixel size should be smaller than the pixel size corresponding to the undersampled image .

[0056] Specifically, determining a target pixel size based on preset requirements, determining the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and an initial pixel size corresponding to the undersampled image, and then collecting the undersampled image output by the astronomical telescope based on the number to be collected and the micro-displacement method, can include: determining an image to be processed corresponding to the astronomical telescope, and judging whether an image resolution corresponding to the image to be processed meets a preset image resolution condition; if the image resolution corresponding to the image to be processed does not meet the preset image resolution condition, setting the image to be processed as an undersampled image; determining a target pixel size based on preset requirements, and determining an initial pixel size corresponding to the undersampled image, determining a size ratio based on the target pixel size and the initial pixel size, and then determining the number to be collected and the micro-displacement method corresponding to the undersampled image based on the size ratio; and collecting several frames of undersampled images with sub-pixel displacement output by the astronomical telescope using a preset jitter method and based on the number to be collected and the micro-displacement method.

[0057] Step S12: determining a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, so as to stitch the undersampled images together in the size search interval according to the search step and using a Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image.

[0058] In this embodiment, the embodiment of the present application needs to determine the size search interval corresponding to the virtual allocation unit size, such as , and then set the search step size of the virtual allocation unit size, for example 0.1 Specifically, a size search interval, a search step, and a current virtual allocation unit size are determined based on the target pixel size, so that each undersampled image is spliced ​​in the size search interval according to the search step based on the current virtual allocation unit size and using the Drizzle algorithm to obtain a current reconstructed image, including: determining a preset lower limit coefficient and a preset upper limit coefficient based on a preset requirement, and determining a lower limit value of the size search interval based on the target pixel size corresponding to the undersampled image and the preset lower limit coefficient, and then determining an upper limit value of the size search interval based on the target pixel size corresponding to the undersampled image and the preset upper limit coefficient; determining a size search interval of a resampling grid based on the size search interval lower limit value and the size search interval upper limit value, and determining a search step corresponding to the resampling grid and the current virtual allocation unit size based on the target pixel size; using the Drizzle algorithm and based on the current virtual allocation unit size, sequentially processing each undersampled image in the size search interval according to the search step to obtain a current reconstructed image, and processing the current reconstructed image using a preset Fourier transform algorithm to obtain a Fourier transform result.

[0059] Furthermore, the embodiment of the present application needs to perform an initial assignment for the virtual allocation unit size, for example Then, the Drizzle algorithm is run to stitch the undersampled images together to obtain the reconstructed image. .

[0060] Specifically, determining the size search interval, the search step, and the current virtual allocation unit size based on the target pixel size may include: determining the arithmetic mean of the lower limit value of the size search interval and the upper limit value of the size search interval, and determining the current virtual allocation unit size based on the arithmetic mean and the pixel size of the undersampled image, and then determining the search step based on a preset accuracy requirement and the initial pixel size corresponding to the undersampled image.

[0061] Step S13: Determine the Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determine the high-frequency noise power function term and the power spectrum entropy function term based on the Fourier transform result, and determine the current function value based on the high-frequency noise power function term and the power spectrum entropy function term.

[0062] In this embodiment, the objective function needs to be calculated. , then update the virtual allocation unit size assignment, and repeat the above process, and finally output the optimal value of the virtual allocation unit size, that is, the objective function The smallest The value taken is the optimal value.

[0063] In addition, after obtaining the current reconstructed image, the embodiment of the present application needs to determine the Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, to determine the high-frequency noise power function term and the power spectrum entropy function term based on the Fourier transform result, and to determine the current function value based on the high-frequency noise power function term and the power spectrum entropy function term. Specifically, determining the Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determining the high-frequency noise power function item and the power spectrum entropy function item based on the Fourier transform result, and determining the current function value based on the high-frequency noise power function item and the power spectrum entropy function item can include: performing a two-dimensional Fourier transform on the current reconstructed image based on the current virtual allocation unit size to obtain a frequency domain response function corresponding to the current reconstructed image, and determining the power spectrum entropy function and the high-frequency noise power function corresponding to the current reconstructed image based on the frequency domain response function, and then determining a resolvable frequency threshold based on the target pixel size and the initial pixel size; determining the high-frequency noise power function item and the power spectrum entropy function item based on the resolvable frequency threshold using the power spectrum entropy function and the high-frequency noise power function; determining a first weight coefficient corresponding to the high-frequency noise power function item and a second weight coefficient corresponding to the power spectrum entropy function item based on preset requirements, and determining the current function value based on the first weight coefficient, the second weight coefficient, the high-frequency noise power function item and the power spectrum entropy function item.

[0064] Furthermore, determining the current function value based on the high-frequency noise power function item and the power spectrum entropy function item can include: determining the degree of clarity improvement based on the first power spectrum entropy function item corresponding to the current reconstructed image and the second power spectrum entropy function item corresponding to the undersampled image, and determining the degree of cleanliness improvement based on the first high-frequency noise power function item corresponding to the current reconstructed image and the second high-frequency noise power function item corresponding to the undersampled image; determining a first weight coefficient corresponding to the degree of clarity improvement, and determining a second weight coefficient based on the degree of cleanliness improvement, so as to determine a clarity score based on the clarity improvement degree and the first weight coefficient, and determine a cleanliness score based on the cleanliness improvement degree and the second weight coefficient, and then determine the corresponding current function value based on each clarity score and each cleanliness score.

[0065] Step S14: Determine whether the current virtual allocation unit size is within the size search interval; if so, update the current virtual allocation unit size to obtain a new current virtual allocation unit size, and jump back to the step of stitching the undersampled images in the size search interval according to the search step size and using the Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval, and then set the current reconstructed image corresponding to the smallest function value among the current function values ​​as the target image.

[0066] In this embodiment, after obtaining the size search interval, the embodiment of the present application needs to determine whether the current virtual allocation unit size is within the size search interval based on the size search interval, and perform corresponding operations based on the determination result.

[0067] It is worth mentioning that the core idea of ​​the adaptive virtual allocation unit design is to establish an objective function related to the virtual allocation unit size, so as to find the virtual allocation unit size that minimizes the value corresponding to the objective function through an adaptive optimization algorithm. In the process of designing the objective function, the embodiment of the present application is based on two aspects: power spectrum entropy and high-frequency noise power. In a specific embodiment, the power spectrum entropy optimization term can promote the energy concentration of the signal frequency band, and the high-frequency noise power optimization term directly suppresses noise signals that exceed the maximum resolvable frequency of the algorithm theory. The normalized mathematical expression is as follows:

[0068] ;

[0069] ;

[0070] ;

[0071] in, is a comprehensive objective function based on power spectrum entropy and high-frequency noise power, is the objective function term corresponding to the power spectrum entropy, is the objective function term corresponding to the high-frequency noise power, and is the weight coefficient, is the virtual allocation unit size to be optimized, Initial assignment of the virtual allocation unit size, The virtual allocation unit size is The two-dimensional Fourier transform result corresponding to the reconstructed image is, The virtual allocation unit size is The power spectrum of the reconstructed image is is the theoretical maximum resolvable frequency corresponding to the Drizzle algorithm, where the theoretical maximum resolvable frequency is determined by the reconstructed image pixel size and the virtual allocation unit size.

[0072] Specifically, determining whether the current virtual allocation unit size is within the size search interval; if so, updating the current virtual allocation unit size to obtain a new current virtual allocation unit size; and re-jumping to the step of stitching the undersampled images in the size search interval according to the search step size and using the Drizzle algorithm based on the current virtual allocation unit size to obtain the current reconstructed image until the current virtual allocation unit size is no longer within the size search interval. This may include: determining whether the current virtual allocation unit size is within an interval length corresponding to the size search interval; if the current virtual allocation unit size is within the interval length corresponding to the size search interval, increasing the current virtual allocation unit size according to the search step size to obtain a new current virtual allocation unit size; and re-jumping to the step of stitching the undersampled images in the size search interval according to the search step size and using the Drizzle algorithm based on the current virtual allocation unit size to obtain the current reconstructed image; and if the current virtual allocation unit size is not within the interval length corresponding to the size search interval, jumping to the step of setting the current reconstructed image corresponding to the function value with the smallest function value among the current function values ​​as the target image.

[0073] In a specific embodiment, the embodiment of the present application has been verified through simulation experiments. Compared with the multi-star point registration method and the traditional Drizzle algorithm, the embodiment of the present application reduces the PSF reconstruction error by 27% and 12%, respectively, thereby further meeting the accuracy requirements for PSF reconstruction in current actual production processes.

[0074] As can be seen from the above, the present application first needs to determine the target pixel size based on the preset requirements, and determine the number of under-sampled images to be collected and the micro-displacement method based on the target pixel size and the initial pixel size corresponding to the under-sampled image, and then collect the under-sampled image output by the astronomical telescope based on the number to be collected and the micro-displacement method; secondly, determine the size search interval, search step and current virtual allocation unit size based on the target pixel size, so as to splice each under-sampled image according to the search step in the size search interval based on the current virtual allocation unit size and use the Drizzle algorithm to obtain the current reconstructed image; then, determine the Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, so as to obtain the current reconstructed image based on the Fourier transform result. The transformation results determine the high-frequency noise power function term and the power spectrum entropy function term, and the current function value is determined based on the high-frequency noise power function term and the power spectrum entropy function term. Finally, it is determined whether the current virtual allocation unit size is within the size search interval. If so, the current virtual allocation unit size is updated to obtain a new current virtual allocation unit size. The process then jumps back to the step of stitching the undersampled images in the size search interval using the Drizzle algorithm according to the search step size based on the current virtual allocation unit size to obtain the current reconstructed image. This process continues until the current virtual allocation unit size is no longer within the size search interval. The current reconstructed image corresponding to the function value with the smallest function value among the current function values ​​is then set as the target image. In this way, the efficiency of processing undersampled images in astronomical telescopes is improved, thereby enhancing the user experience.

[0075] Accordingly, see Figure 2 As shown, the present application also provides an undersampling image processing device for an astronomical telescope, comprising:

[0076] The undersampled image determination module 11 is configured to determine a target pixel size based on preset requirements, determine the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and the initial pixel size corresponding to the undersampled image, and then collect the undersampled image output by the astronomical telescope based on the number of undersampled images to be collected and the micro-displacement method; the target pixel size is smaller than the initial pixel size;

[0077] a reconstructed image determination module 12, configured to determine a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, so as to combine the undersampled images in the size search interval according to the search step and using a Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image;

[0078] a function value determination module 13, configured to determine a Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determine a high-frequency noise power function term and a power spectrum entropy function term based on the Fourier transform result, and determine a current function value based on the high-frequency noise power function term and the power spectrum entropy function term;

[0079] The target image determination module 14 is configured to determine whether the current virtual allocation unit size is within the size search interval. If so, the target image determination module 14 updates the current virtual allocation unit size to obtain a new current virtual allocation unit size, and then jumps back to the step of stitching the undersampled images within the size search interval according to the search step size and using the Drizzle algorithm to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval. The target image is then set as the target image, and the target image is determined based on the current virtual allocation unit size.

[0080] In some specific implementations, the undersampled image determination module 11 may specifically include:

[0081] an image determination unit, configured to determine an image to be processed corresponding to the astronomical telescope, and determine whether an image resolution corresponding to the image to be processed satisfies a preset image resolution condition; if the image resolution corresponding to the image to be processed does not satisfy the preset image resolution condition, setting the image to be processed as an undersampled image;

[0082] a size ratio determining unit, configured to determine a target pixel size based on preset requirements, and determine an initial pixel size corresponding to the undersampled image, determine a size ratio based on the target pixel size and the initial pixel size, and then determine a number of pixels to be collected and a micro-displacement method corresponding to the undersampled image based on the size ratio;

[0083] The under-sampled image acquisition unit is used to acquire a plurality of frames of under-sampled images with sub-pixel displacement output by the astronomical telescope using a preset dithering method and based on the number to be acquired and the micro-displacement method.

[0084] In some specific implementations, the reconstructed image determination module 12 may specifically include:

[0085] a coefficient determination unit, configured to determine a preset lower limit coefficient and a preset upper limit coefficient based on preset requirements, determine a lower limit value of a size search interval based on the target pixel size corresponding to the undersampled image and the preset lower limit coefficient, and then determine an upper limit value of the size search interval based on the target pixel size corresponding to the undersampled image and the preset upper limit coefficient;

[0086] a size search interval determining unit, configured to determine a size search interval of a resampling grid based on the size search interval lower limit value and the size search interval upper limit value, and determine a search step corresponding to the resampling grid and a current virtual allocation unit size based on the target pixel size;

[0087] A Fourier transform result determination unit is configured to process each of the undersampled images in sequence according to the search step size in the size search interval using a Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image, and process the current reconstructed image using a preset Fourier transform algorithm to obtain a Fourier transform result.

[0088] In some specific implementations, the reconstructed image determination module 12 may specifically include:

[0089] a search step determination unit, configured to determine an arithmetic mean of a lower limit value of the size search interval and an upper limit value of the size search interval, determine a current virtual allocation unit size based on the arithmetic mean value and a pixel size of the undersampled image, and then determine a search step based on a preset accuracy requirement and an initial pixel size corresponding to the undersampled image.

[0090] In some specific implementations, the function value determination module 13 may specifically include:

[0091] a frequency domain response function determining unit, configured to perform a two-dimensional Fourier transform on the current reconstructed image based on the current virtual allocation unit size to obtain a frequency domain response function corresponding to the current reconstructed image, determine a power spectrum entropy function and a high-frequency noise power function corresponding to the current reconstructed image based on the frequency domain response function, and then determine a resolvable frequency threshold based on the target pixel size and the initial pixel size;

[0092] a function term determining unit, configured to determine a high-frequency noise power function term and a power spectrum entropy function term based on the resolvable frequency threshold using the power spectrum entropy function and the high-frequency noise power function;

[0093] The first function value determination subunit is used to determine a first weight coefficient corresponding to the high-frequency noise power function item and a second weight coefficient corresponding to the power spectrum entropy function item based on preset requirements, so as to determine the current function value based on the first weight coefficient, the second weight coefficient, the high-frequency noise power function item and the power spectrum entropy function item.

[0094] In some specific implementations, the function value determination module 13 may specifically include:

[0095] an improvement degree determining unit, configured to determine a clarity improvement degree based on a first power spectrum entropy function item corresponding to the current reconstructed image and a second power spectrum entropy function item corresponding to the undersampled image, and to determine a cleanliness improvement degree based on a first high-frequency noise power function item corresponding to the current reconstructed image and a second high-frequency noise power function item corresponding to the undersampled image;

[0096] The second function value determination subunit is used to determine a first weight coefficient corresponding to the degree of clarity improvement, and to determine a second weight coefficient based on the degree of cleanliness improvement, so as to determine a clarity score based on the degree of clarity improvement and the first weight coefficient, and to determine a cleanliness score based on the degree of cleanliness improvement and the second weight coefficient, and then determine a corresponding current function value based on each of the clarity scores and each of the cleanliness scores.

[0097] In some specific implementations, the target image determination module 14 may specifically include:

[0098] a virtual allocation unit size determination unit, configured to determine whether the current virtual allocation unit size is within the interval length corresponding to the size search interval; if the current virtual allocation unit size is within the interval length corresponding to the size search interval, increasing the current virtual allocation unit size according to the search step length to obtain a new current virtual allocation unit size; and re-jumping to the step of combining the undersampled images in the size search interval according to the search step length and using the Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image;

[0099] The step jumping unit is configured to jump to the step of setting the current reconstructed image corresponding to the smallest function value among the current function values ​​as the target image if the current virtual allocation unit size is not within the interval length corresponding to the size search interval.

[0100] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 3 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of this diagram should not be construed as limiting the scope of application of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps of the undersampled image processing method for an astronomical telescope disclosed in any of the aforementioned embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0101] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device. The communication protocol it follows is any communication protocol that can be applied to the technical solution of this application and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world. Its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0102] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0103] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of implementing the undersampling image processing method for an astronomical telescope executed by the electronic device 20 as disclosed in any of the aforementioned embodiments, the computer program 222 may further include computer programs capable of implementing other specific tasks.

[0104] Furthermore, this application discloses a computer-readable medium for storing a computer program; wherein, when executed by a processor, the computer program implements the aforementioned undersampling image processing method for an astronomical telescope. The specific steps of this method can be found in the corresponding contents disclosed in the aforementioned embodiments and will not be repeated here.

[0105] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Reference can be made to the descriptions of the identical or similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and the relevant parts can be referred to the descriptions of the methods.

[0106] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of media known in the art.

[0108] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0109] The above is a detailed introduction to the technical solution provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for processing undersampled images of an astronomical telescope, characterized in that: include: Determining a target pixel size based on preset requirements, and determining the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and an initial pixel size corresponding to the undersampled image, and then collecting the undersampled image output by the astronomical telescope based on the number of images to be collected and the micro-displacement method; The target pixel size is smaller than the initial pixel size; determining a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, so as to stitch the undersampled images together using a Drizzle algorithm in the size search interval according to the search step and based on the current virtual allocation unit size to obtain a current reconstructed image; Determining a Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determining a high-frequency noise power function term and a power spectrum entropy function term based on the Fourier transform result, and determining a current function value based on the high-frequency noise power function term and the power spectrum entropy function term; Determine whether the current virtual allocation unit size is within the size search interval; if so, update the current virtual allocation unit size to obtain a new current virtual allocation unit size, and jump back to the step of stitching the undersampled images according to the search step size in the size search interval based on the current virtual allocation unit size and using the Drizzle algorithm to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval, and then set the current reconstructed image corresponding to the function value with the smallest function value among the current function values ​​as the target image.

2. The under-sampling image processing method for an astronomical telescope according to claim 1, characterized in that: The method includes determining a target pixel size based on a preset requirement, determining the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and an initial pixel size corresponding to the undersampled image, and then collecting the undersampled image output by the astronomical telescope based on the number of undersampled images to be collected and the micro-displacement method, including: determining an image to be processed corresponding to the astronomical telescope, and determining whether an image resolution corresponding to the image to be processed meets a preset image resolution condition; if the image resolution corresponding to the image to be processed does not meet the preset image resolution condition, setting the image to be processed as an undersampled image; Determining a target pixel size based on preset requirements and determining an initial pixel size corresponding to the undersampled image, determining a size ratio based on the target pixel size and the initial pixel size, and then determining a number of pixels to be collected and a micro-displacement method corresponding to the undersampled image based on the size ratio; A preset jittering method is used and based on the number to be collected and the micro-displacement method, several frames of under-sampled images with sub-pixel displacement output by the astronomical telescope are collected.

3. The under-sampling image processing method for an astronomical telescope according to claim 1, characterized in that: The step of determining a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, and combining the undersampled images in the size search interval according to the search step and using a Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image, includes: Determining a preset lower limit coefficient and a preset upper limit coefficient based on preset requirements, determining a lower limit value of a size search interval based on the target pixel size corresponding to the undersampled image and the preset lower limit coefficient, and then determining an upper limit value of the size search interval based on the target pixel size corresponding to the undersampled image and the preset upper limit coefficient; Determining a size search interval of a resampling grid based on the size search interval lower limit value and the size search interval upper limit value, and determining a search step size and a current virtual allocation unit size corresponding to the resampling grid based on the target pixel size; The undersampled images are processed sequentially in the size search interval according to the search step size using a Drizzle algorithm and based on the current virtual allocation unit size to obtain a current reconstructed image, and the current reconstructed image is processed using a preset Fourier transform algorithm to obtain a Fourier transform result.

4. The under-sampling image processing method for an astronomical telescope according to claim 3, characterized in that: The determining of a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size includes: An arithmetic mean of a lower limit value of the size search interval and an upper limit value of the size search interval is determined, and a current virtual allocation unit size is determined based on the arithmetic mean value and a pixel size of the undersampled image. Then, a search step size is determined based on a preset accuracy requirement and an initial pixel size corresponding to the undersampled image.

5. The under-sampling image processing method for an astronomical telescope according to claim 1, characterized in that: The determining of a Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determining a high-frequency noise power function term and a power spectrum entropy function term based on the Fourier transform result, and determining a current function value based on the high-frequency noise power function term and the power spectrum entropy function term, includes: performing a two-dimensional Fourier transform on the current reconstructed image based on the current virtual allocation unit size to obtain a frequency domain response function corresponding to the current reconstructed image, determining a power spectrum entropy function and a high-frequency noise power function corresponding to the current reconstructed image based on the frequency domain response function, and then determining a resolvable frequency threshold based on the target pixel size and the initial pixel size; Determining a high-frequency noise power function term and a power spectrum entropy function term based on the resolvable frequency threshold using the power spectrum entropy function and the high-frequency noise power function; Based on preset requirements, a first weight coefficient corresponding to the high-frequency noise power function item and a second weight coefficient corresponding to the power spectrum entropy function item are determined to determine the current function value based on the first weight coefficient, the second weight coefficient, the high-frequency noise power function item and the power spectrum entropy function item.

6. The under-sampling image processing method for an astronomical telescope according to claim 5, characterized in that: The determining of the current function value based on the high-frequency noise power function term and the power spectrum entropy function term includes: Determining a degree of clarity improvement based on a first power spectrum entropy function item corresponding to the current reconstructed image and a second power spectrum entropy function item corresponding to the undersampled image, and determining a degree of cleanliness improvement based on a first high-frequency noise power function item corresponding to the current reconstructed image and a second high-frequency noise power function item corresponding to the undersampled image; Determine a first weight coefficient corresponding to the degree of clarity improvement, and determine a second weight coefficient based on the degree of cleanliness improvement, so as to determine a clarity score based on the degree of clarity improvement and the first weight coefficient, and determine a cleanliness score based on the degree of cleanliness improvement and the second weight coefficient, and then determine a corresponding current function value based on each of the clarity scores and each of the cleanliness scores.

7. The under-sampling image processing method for an astronomical telescope according to any one of claims 1 to 6, characterized in that: The step of determining whether the current virtual allocation unit size is within the size search interval and, if so, updating the current virtual allocation unit size to obtain a new current virtual allocation unit size, and re-jumping to the step of stitching the undersampled images according to the search step size in the size search interval using the Drizzle algorithm to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval, includes: determining whether the current virtual allocation unit size is within the interval length corresponding to the size search interval; if the current virtual allocation unit size is within the interval length corresponding to the size search interval, increasing the current virtual allocation unit size according to the search step to obtain a new current virtual allocation unit size, and re-jumping to the step of stitching the undersampled images in the size search interval according to the search step based on the current virtual allocation unit size using the Drizzle algorithm to obtain a current reconstructed image; If the current virtual allocation unit size is not within the interval length corresponding to the size search interval, the process jumps to the step of setting the current reconstructed image corresponding to the smallest function value among the current function values ​​as the target image.

8. An undersampling image processing device for an astronomical telescope, characterized in that: include: an undersampled image determination module, configured to determine a target pixel size based on preset requirements, determine the number of undersampled images to be collected and a micro-displacement method based on the target pixel size and an initial pixel size corresponding to the undersampled image, and then collect the undersampled image output by the astronomical telescope based on the number of undersampled images to be collected and the micro-displacement method; the target pixel size is smaller than the initial pixel size; a reconstructed image determination module, configured to determine a size search interval, a search step, and a current virtual allocation unit size based on the target pixel size, so as to combine the undersampled images in the size search interval according to the search step and using a Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image; a function value determination module, configured to determine a Fourier transform result corresponding to the current reconstructed image based on the current virtual allocation unit size, determine a high-frequency noise power function term and a power spectrum entropy function term based on the Fourier transform result, and determine a current function value based on the high-frequency noise power function term and the power spectrum entropy function term; a target image determination module, configured to determine whether the current virtual allocation unit size is within the size search interval; if so, to update the current virtual allocation unit size to obtain a new current virtual allocation unit size; and to jump back to the step of stitching the undersampled images within the size search interval according to the search step size and using the Drizzle algorithm based on the current virtual allocation unit size to obtain a current reconstructed image, until the current virtual allocation unit size is no longer within the size search interval; and then setting the current reconstructed image corresponding to the smallest function value among the current function values ​​as the target image.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the undersampling image processing method for an astronomical telescope according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that Used to store a computer program, wherein when the computer program is executed by a processor, the under-sampling image processing method for an astronomical telescope according to any one of claims 1 to 7 is implemented.

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