Hartmann wavefront sensor wavefront detection method and device based on sub-aperture time domain information windowing

Abstract

The invention discloses a Hartmann wavefront sensor wavefront detection method and device based on sub-aperture time domain information windowing, and belongs to the technical field of wavefront detection.The method comprises the steps that a light spot array image of a Hartmann wavefront sensor with dynamically fluctuating light intensity is collected; segmenting effective sub-apertures according to the design arrangement of the Hartmann wavefront sensor sub-apertures, and obtaining the mass center positions and signal-to-noise ratios of the preorder multi-frame effective sub-apertures; calculating the signal-to-noise ratio of each effective sub-aperture of the current frame of light spot array image by using the same method as the step 1; selecting a light spot positioning algorithm according to the signal-to-noise ratio of each effective sub-aperture obtained in the step 2; and obtaining a slope vector of the detection wavefront of the current frame according to the centroid positions of all the effective sub-apertures of the current frame, and reconstructing the wavefront by adopting a wavefront restoration algorithm. According to the invention, the wavefront detection precision of the Hartmann wavefront sensor in a complex environment can be improved, the sudden change of the wavefront slope can be avoided by means of the time domain continuity constraint, and the system robustness is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of wavefront detection, and particularly relates to a wavefront detection method and device for a Hartmann wavefront sensor based on windowing of sub-aperture time-domain information. Background Art

[0002] The Hartmann wavefront sensor has the advantages of simple structure and high light energy utilization rate, and is widely used in many fields such as astronomical observation, adaptive optics, and laser communication. Its basic principle is to divide the incident light beam into multiple sub-beams through a microlens array, and each sub-beam is focused on a photodetector to form a light spot. By measuring the centroid position offset of the light spot and using a wavefront reconstruction algorithm, the wavefront phase distribution of the entire incident light beam can be obtained.

[0003] In actual application scenarios, factors such as atmospheric turbulence and transmission distance will cause fluctuations in the near-field light intensity of the light beam, resulting in a scintillation phenomenon. When performing wavefront detection, the scintillation phenomenon, together with the sensor dynamic range limitation and multi-source noise, makes the spot array image collected by the Hartmann wavefront sensor show an uneven intensity distribution and a spatio-temporal dynamic change in the signal-to-noise ratio. The spot signals of sub-apertures with a low signal-to-noise ratio are overwhelmed by noise, resulting in a large centroid positioning error, a decrease in the reconstruction accuracy of the wavefront sensor, and insufficient wavefront reconstruction accuracy, thereby restricting high-precision wavefront distortion correction and beam transmission efficiency. Existing methods rely on single-frame Hartmann image information and do not fully utilize the image data in the time series, resulting in insufficient accuracy and anti-interference ability of wavefront reconstruction. There may be a large centroid calculation error when the noise interference is large, and an unavoidable slope mutation in time will affect the stability of the system. Summary of the Invention

[0004] To solve the above technical problems, the present invention adopts the following technical solutions:

[0005] A wavefront detection method for a Hartmann wavefront sensor based on windowing of sub-aperture time-domain information, comprising:

[0006] Step 1, collecting a spot array image of a Hartmann wavefront sensor with dynamically fluctuating light intensity, dividing out effective sub-apertures according to the designed arrangement of the sub-apertures of the Hartmann wavefront sensor, and obtaining the centroid positions and signal-to-noise ratios of multiple previous frames of effective sub-apertures;

[0007] Step 2, calculating the signal-to-noise ratio of each effective sub-aperture of the current frame spot array image using the same method as in Step 1;

[0008] Step 3, selecting a spot positioning algorithm according to the signal-to-noise ratio of each effective sub-aperture obtained in Step 2;

[0009] Step 4, obtaining the slope vector of the detected wavefront of the current frame according to the centroid positions of all effective sub-apertures of the current frame, and reconstructing the wavefront using a wavefront reconstruction algorithm.

[0010] A Hartmann wavefront sensor wavefront detection device based on sub-aperture time-domain information windowing, comprising the following modules:

[0011] A centroid position and signal-to-noise ratio acquisition module, which collects the spot array image of a Hartmann wavefront sensor with dynamically fluctuating light intensity, divides out effective sub-apertures according to the designed arrangement of the sub-apertures of the Hartmann wavefront sensor, and obtains the centroid position and signal-to-noise ratio of multiple previous frames of effective sub-apertures;

[0012] A signal-to-noise ratio calculation module, which calculates the signal-to-noise ratio of each effective sub-aperture of the current frame spot array image by using the same method as the method for obtaining the signal-to-noise ratio in the centroid position and signal-to-noise ratio acquisition module;

[0013] A spot positioning algorithm selection module, which selects a spot positioning algorithm according to the signal-to-noise ratio of each effective sub-aperture obtained by the signal-to-noise ratio calculation module;

[0014] A reconstructed wavefront module, which obtains the slope vector of the detected wavefront of the current frame according to the centroid positions of all effective sub-apertures of the current frame, and reconstructs the wavefront by using a wavefront restoration algorithm.

[0015] An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the program, the steps of the above-mentioned Hartmann wavefront sensor wavefront detection method based on sub-aperture time-domain information windowing are implemented.

[0016] A non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned Hartmann wavefront sensor wavefront detection method based on sub-aperture time-domain information windowing are implemented.

[0017] The present invention has the following beneficial effects:

[0018] The present invention uses the sub-aperture historical frame information to perform local windowing on sub-apertures with low signal-to-noise ratio, suppresses the influence of noise on wavefront detection, and improves the wavefront restoration accuracy and robustness of the Hartmann wavefront sensor under near-field light intensity flicker.

[0019] The present invention estimates the centroid position of the current frame sub-aperture based on the historical frame sub-aperture information, and combines the windowing technology with the time-domain centroid as the window center, effectively overcoming the problem of insufficient anti-noise performance caused by the traditional method relying on the peak light intensity to determine the window center, and significantly improving the wavefront detection accuracy of the Hartmann wavefront sensor in a complex environment with intense near-field light intensity fluctuations and high noise.

[0020] The present invention utilizes the temporal continuity of the centroid positions of adjacent frame sub-apertures to constrain the temporal variation range of the centroid offset, enhancing the robustness of wavefront slope calculation, avoiding damage to subsequent actuators such as deformable mirrors caused by slope mutations in time, and improving the overall reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flowchart of the wavefront detection method of the Hartmann wavefront sensor based on sub-aperture temporal information windowing according to the present invention;

[0022] Figure 2 is a comparison diagram of the slope change of a single sub-aperture;

[0023] Figure 3 is a comparison diagram of the reconstructed wavefront and reconstructed residual of the input wavefront; where, (a) is the input wavefront, (b) is the reconstructed wavefront by the adaptive threshold method, (c) is the reconstructed wavefront by the peak windowing method, (d) is the reconstructed wavefront by the temporal windowing method, (e) is the reconstructed residual without noise, (f) is the reconstructed residual by the adaptive threshold method, (g) is the reconstructed residual by the peak windowing method, (h) is the reconstructed residual by the temporal windowing method;

[0024] Figure 4(a) is a comparison diagram of the rms values of the wavefront reconstruction residuals of the Hartmann spot array images under the continuous 1000-frame near-field light intensity fluctuations of the adaptive threshold method and the temporal windowing method;

[0025] Figure 4(b) is a comparison diagram of the rms values of the wavefront reconstruction residuals of the Hartmann spot array images under the continuous 1000-frame near-field light intensity fluctuations of the peak windowing method and the temporal windowing method. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0027] In this embodiment, the laser wavelength is 1064 nm, the sampling frame rate of the Hartmann wavefront sensor is 1000 Hz, the number of sub-apertures , the number of near-field sampling points of a single sub-aperture , there is a 30% central obscuration, the focal length of the microlens is 7.9 mm, and the CCD pixel size .

[0028] Figure 1This is the flowchart of the wavefront detection method for a Hartmann wavefront sensor based on windowing of sub-aperture time-domain information. Based on the time-domain continuity feature of the sub-spot positions, the present invention estimates the low signal-to-noise ratio sub-spot positions at the current moment using the previous high signal-to-noise ratio sub-spot positions, and performs secondary windowing based on these positions to reduce the influence of detection noise on centroid positioning and improve the wavefront reconstruction accuracy. Specifically, it includes:

[0029] Step 1: Collect the spot array image of a Hartmann wavefront sensor with dynamically fluctuating light intensity, divide the effective sub-apertures according to the designed layout of the sub-apertures of the Hartmann wavefront sensor, and obtain the centroid positions and signal-to-noise ratios of the previous multiple frames of effective sub-apertures.

[0030] The methods for obtaining the centroid positions include the threshold method, the window method, the correlation method, or other centroid positioning methods. The methods for obtaining the signal-to-noise ratios include the mean signal-to-noise ratio method, the peak signal-to-noise ratio method, or other methods for obtaining the signal-to-noise ratio. In this embodiment, the four-corner threshold method is used to calculate the centroid position, and the peak signal-to-noise ratio method is used to calculate the signal-to-noise ratio.

[0031] The four-corner threshold method counts the gray values of the pixels in the four corners of the sub-aperture or window as the noise region, calculates the noise threshold by calculating its mean and standard deviation, and calculates the centroid position after subtracting the threshold from the gray value of the sub-aperture pixels. The specific process is as follows:

[0032] Step 1.1: Count the gray values of the pixels in the four corners of the sub-aperture or window :

[0033] ;

[0034] Among them, is the gray value of the sub-aperture pixel, is the set of gray values of the pixels in the four-corner region, , are the coordinate indices of the gray value of the sub-aperture pixel respectively.

[0035] Step 1.2: Obtain the mean and standard deviation of the gray values in the four-corner region, and then the noise threshold is:

[0036] .

[0037] Step 1.3: Calculate the centroid position using the result of subtracting the noise threshold from the sub-aperture pixel gray value :

[0038] ;

[0039] ;

[0040] wherein, is the sub-aperture pixel gray value minus the noise threshold of the sub-aperture pixel gray value after subtraction, is the centroid position of the sub-aperture, is the pixel point coordinate index of, and are the number of rows and columns of the sub-aperture respectively.

[0041] The calculation formula of the peak signal-to-noise ratio method is as follows:

[0042] ;

[0043] wherein, is the maximum gray value within the sub-aperture, and are the mean and standard deviation of the noise within the sub-aperture respectively, is the signal-to-noise ratio of the sub-aperture.

[0044] Step 2: Calculate the signal-to-noise ratio of each effective sub-aperture in the current frame spot array image using the same peak signal-to-noise ratio method as in Step 1.

[0045] Step 3: Select a spot positioning algorithm according to the signal-to-noise ratio of each effective sub-aperture obtained in Step 2, specifically including:

[0046] When the sub-aperture signal-to-noise ratio is higher than the fixed threshold V (the fixed threshold for dividing high and low signal-to-noise ratios is set to 7 according to experience), use the same four-corner threshold method as in Step 1 (or other types of centroid positioning methods) to calculate the centroid position and update the centroid position and signal-to-noise ratio data in Step 1 (wherein, use the data in Step 2 to update the signal-to-noise ratio. Step 1 calculates the centroid positions of the previous multi-frame effective sub-apertures, and Step 3.1 calculates the centroid position of the current frame. "Update" means recording the data of the current frame for use in the next frame image. For the next frame, the current frame is the previous frame).

[0047] When the sub-aperture signal-to-noise ratio is lower than the fixed threshold V (the fixed threshold for dividing high and low signal-to-noise ratios is set to 7 according to experience), estimate the position of the current low-signal-to-noise sub-spot using the position of the previous high-signal-to-noise sub-spot, and perform windowing on the current frame low-signal-to-noise sub-aperture with the centroid position of the optimal sub-aperture selected from the previous multi-frame effective sub-apertures as the window center. This process is the time-domain windowing method. Use the same four-corner threshold method as in Step 1 (or other types of centroid positioning methods) to calculate the centroid position and update the centroid position and signal-to-noise ratio data within the small window. The implementation method of the time-domain windowing method is as follows:

[0048] Step 3.1, select the high signal-to-noise ratio sub-aperture with a signal-to-noise ratio higher than the fixed threshold of 7 and closest to the current frame from the pre-stored signal-to-noise ratio data of the previous n frames (taking 3 frames as an example) of sub-apertures as the optimal sub-aperture; if there is no qualified one, select the sub-aperture with the highest signal-to-noise ratio among the previous 3 frames of sub-apertures as the optimal sub-aperture:

[0049] ;

[0050] ;

[0051] wherein, is the set of time-domain positions of the sub-apertures in the previous 3 frames, 、 、 are the time-domain positions of the sub-apertures in the previous 1st, 2nd, and 3rd frames respectively, is the time-domain position of the sub-aperture in the current frame, is the time-domain position at which the signal-to-noise ratio of the sub-aperture is located, represents taking the maximum element in the set, represents taking the independent variable value that makes the function value the largest, is the time-domain position of the optimal sub-aperture, means that there exists a time-domain position belonging to the set such that the signal-to-noise ratio of the sub-aperture at the time-domain position is greater than 7.

[0052] Step 3.2, based on the centroid position of the determined optimal sub-aperture, generate a mask template of a window with the centroid position as the window center, a size of (which can also be other sizes) and a square shape (which can also be other shapes):

[0053] ;

[0054] wherein, and are the centroid positions of the optimal sub-aperture, is the mask template of the window, represents taking the absolute value is the coordinate index of the pixel point and is the window radius.

[0055] Step 3.3, attach the mask template of this window to the low signal-to-noise ratio sub-aperture of the current frame to complete the time-domain windowing operation.

[0056] ;

[0057] wherein, is the pixel gray value of the sub-aperture with low signal-to-noise ratio after windowing, represents the Hadamard product (i.e., element-wise multiplication), is the pixel gray value of the sub-aperture with low signal-to-noise ratio in the current frame, is the mask template of the window.

[0058] Step 4: Obtain the slope vector of the detected wavefront in the current frame according to the centroid positions of all effective sub-apertures in the current frame, and use a wavefront reconstruction algorithm, such as the modal method (modal method of Zernike polynomial fitting), regional method to reconstruct the wavefront.

[0059] such as Figure 2 shown, which gives the comparison diagram of the slope changes of 20 consecutive frames of a single sub-aperture. It can be seen that the time-domain windowing method of the present invention has smaller slope calculation error and stronger robustness of the slope calculation result compared with the adaptive threshold method and the peak windowing method.

[0060] Figure 3 is the comparison diagram of the reconstructed wavefront and the reconstruction residual of the input wavefront; among them, (a) is the input wavefront, (b) is the reconstructed wavefront of the adaptive threshold method, (c) is the reconstructed wavefront of the peak windowing method, (d) is the reconstructed wavefront of the time-domain windowing method, (e) is the reconstruction residual without noise, (f) is the reconstruction residual of the adaptive threshold method, (g) is the reconstruction residual of the peak windowing method, (h) is the reconstruction residual of the time-domain windowing method; the RMS values of the wavefront reconstruction residuals of noiseless, adaptive threshold method, peak windowing method and time-domain windowing method are respectively 、 、 and where is the wavelength unit. The results show that the wavefront reconstruction accuracy of the time-domain windowing method is improved by about 59% compared with the two centroid localization methods based on single-frame image information, the adaptive threshold method and the peak windowing method.

[0061] Figure 4(a) is the comparison diagram of the RMS values of the wavefront reconstruction residuals of the Hartmann spot array images under the near-field light intensity fluctuations of 1000 consecutive frames of the adaptive threshold method and the time-domain windowing method, and Figure 4(b) is the comparison diagram of the RMS values of the wavefront reconstruction residuals of the Hartmann spot array images under the near-field light intensity fluctuations of 1000 consecutive frames of the peak windowing method and the time-domain windowing method. The means of the RMS values of the wavefront reconstruction residuals of 1000 frames of the adaptive threshold method, the peak windowing method and the time-domain windowing method are respectively 、 and where is the wavelength unit. Compared with the previous two methods, the time-domain windowing method improves the wavefront reconstruction accuracy by about 43% and 51% respectively.

[0062] In view of the problems of large centroid positioning errors and insufficient wavefront reconstruction accuracy of existing single-frame image centroid positioning methods in the presence of light intensity flicker and dynamic noise, the present invention proposes a wavefront detection method for a Hartmann wavefront sensor based on windowing of sub-aperture time-domain information. This method is based on the time-domain continuity feature of the wavefront phase, uses the positions of previous high signal-to-noise ratio sub-spots to estimate the positions of current low signal-to-noise ratio sub-spots, and performs windowing at these positions to suppress the influence of noise on centroid positioning and improve the wavefront reconstruction accuracy. The present invention can improve the wavefront detection accuracy of the Hartmann wavefront sensor in complex environments and avoid sudden changes in wavefront slope by means of time-domain continuity constraints, enhancing the robustness of the system.

[0063] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0064] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0065] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, causing a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks. Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.

[0067] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A wavefront detection method of a Hartmann wavefront sensor based on sub-aperture time domain information windowing, characterized in that: include: Step 1, collecting a spot array image of a Hartmann wavefront sensor with dynamically fluctuating light intensity, segmenting effective sub-apertures according to the designed arrangement of sub-apertures of the Hartmann wavefront sensor, and obtaining the centroid position and signal-to-noise ratio of the previous multiple frames of effective sub-apertures; Step 2, using the same method as step 1 to obtain the signal-to-noise ratio of each effective sub-aperture of the spot array image of the current frame; Step 3, selecting a spot positioning algorithm according to the signal-to-noise ratio of each effective sub-aperture obtained in step 2; Step 4: Obtain the slope vector of the detection wavefront of the current frame according to the centroid positions of all effective sub-apertures of the current frame, and reconstruct the wavefront using a wavefront restoration algorithm.

2. The wavefront detection method of Hartmann wavefront sensor based on sub-aperture time domain information windowing according to claim 1 is characterized in that: In step 1, the method for obtaining the centroid position includes a threshold method, a window method, a correlation method or other centroid positioning methods, and the method for obtaining the signal-to-noise ratio includes a mean signal-to-noise ratio method, a peak signal-to-noise ratio method or other methods for obtaining the signal-to-noise ratio.

3. The wavefront detection method of Hartmann wavefront sensor based on sub-aperture time domain information windowing according to claim 1 is characterized in that: In step 1, the method for obtaining the centroid position is a four-corner threshold method, which specifically includes: Step 1.1, counting the grayscale values ​​of pixels in the four corners of the sub-aperture or window; Step 1.2: Get the mean gray value of the four corner areas and standard deviation , and then the noise threshold for: ； Step 1.3, use sub-aperture pixel grayscale value Noise reduction threshold The result is to calculate the centroid position: ； ； in, is the subaperture pixel grayscale value Noise reduction threshold The grayscale value of the sub-aperture pixel after is the centroid position of the subaperture, It's a pixel The coordinate index of , are the number of rows and columns of subapertures, respectively.

4. The wavefront detection method of Hartmann wavefront sensor based on sub-aperture time domain information windowing according to claim 1 is characterized in that: In step 1, the method for obtaining the signal-to-noise ratio is the peak signal-to-noise ratio method, and the calculation formula of the peak signal-to-noise ratio method is as follows: ； in, is the maximum grayscale value within the sub-aperture, and are the mean and standard deviation of the noise within the sub-aperture, is the signal-to-noise ratio of the subaperture.

5. The wavefront detection method of Hartmann wavefront sensor based on sub-aperture time domain information windowing according to claim 1, characterized in that: Step 3 specifically includes: When the subaperture signal-to-noise ratio is higher than a fixed threshold V, the centroid position is calculated using the centroid positioning method and the centroid position and signal-to-noise ratio of step 1 are updated; When the sub-aperture signal-to-noise ratio is lower than the fixed threshold V, the position of the sub-spot with low signal-to-noise ratio at the current moment is estimated using the position of the previous high-SNR sub-spot. The centroid position of the optimal sub-aperture selected from the effective sub-apertures of the previous multiple frames is used as the window center to open a window on the low-SNR sub-aperture of the current frame, that is, the time-domain windowing method. The centroid positioning method is used in the small window to calculate the centroid position and update the centroid position and signal-to-noise ratio data.

6. The wavefront detection method of Hartmann wavefront sensor based on sub-aperture time domain information windowing according to claim 5 is characterized in that: In step 3, the time domain windowing method includes: Step 3.1, selecting a high signal-to-noise ratio subaperture whose signal-to-noise ratio is higher than a fixed threshold V and is closest to the current frame from the pre-stored signal-to-noise ratio data of the subapertures of the previous n frames as the optimal subaperture; if no subaperture meets the conditions, selecting the subaperture with the highest signal-to-noise ratio among the subapertures of the previous n frames as the optimal subaperture; Step 3.2, based on the determined centroid position of the optimal sub-aperture, generate a mask of a window with the centroid position as the window center: Step 3.3, attaching the mask of the window to the low signal-to-noise ratio sub-aperture of the current frame to complete the time domain windowing operation; ； in, is the grayscale value of the low signal-to-noise ratio sub-aperture pixel after windowing, represents the Hadamard product, is the gray value of the low signal-to-noise ratio sub-aperture pixel in the current frame, The mask for the window.

7. The wavefront detection method of Hartmann wavefront sensor based on sub-aperture time domain information windowing according to claim 1, characterized in that: The wavefront restoration algorithm described in step 4 includes a pattern method, a regional method or other wavefront restoration algorithms.

8. A Hartmann wavefront sensor wavefront detection device based on sub-aperture time domain information windowing, characterized in that: Includes the following modules: The centroid position and signal-to-noise ratio acquisition module collects the spot array image of the Hartmann wavefront sensor with dynamically fluctuating light intensity, divides the effective sub-aperture according to the design arrangement of the sub-aperture of the Hartmann wavefront sensor, and obtains the centroid position and signal-to-noise ratio of the previous multiple frames of effective sub-aperture; A signal-to-noise ratio calculation module, which calculates the signal-to-noise ratio of each effective sub-aperture of the spot array image of the current frame using the same method as the method for obtaining the signal-to-noise ratio in the centroid position and signal-to-noise ratio acquisition module; A spot positioning algorithm selection module selects a spot positioning algorithm according to the signal-to-noise ratio of each effective sub-aperture obtained by the signal-to-noise ratio calculation module; The wavefront reconstruction module obtains the slope vector of the detection wavefront of the current frame according to the centroid position of all effective sub-apertures in the current frame, and reconstructs the wavefront using the wavefront restoration algorithm.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the wavefront detection method of the Hartmann wavefront sensor based on sub-aperture time domain information windowing as described in any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the wavefront detection method of the Hartmann wavefront sensor based on sub-aperture time domain information windowing as described in any one of claims 1 to 7 are implemented.

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