Non-linear wavefront curvature sensing method based on double-pupil plane

Abstract

The invention discloses a non-linear wavefront curvature sensing method based on a double-pupil plane, relates to the field of wavefront sensing for an atmospheric turbulence field, and is technically characterized by aiming at the problem that the non-linear wavefront curvature sensing method is low in efficiency of pupil image acquisition and iterative computation. And taking the solved wavefront of the historical frame as an initial value of iterative calculation of the current frame. The wavefront sensing precision and speed of a traditional method can be effectively improved by taking a historical frame wavefront phase as a constraint for solving a current frame by utilizing the characteristic that the wavefront information of a turbulent flow field has local information correlation of time sequence variation. In addition, application in a scene with limited light intensity is explored, that is, the effect of executing nonlinear wavefront curvature sensing by using a smaller number of pupil planes is explored. It is found that the wavefront sensing precision and the faster wavefront convergence speed which are equivalent to those of a traditional four-separation-pupil-plane method can be obtained only through the information of the two separation pupil planes.

Description

Technical Field

[0001] The present invention belongs to the technical field of atmospheric wavefront sensing, and in particular relates to a wavefront sensing method based on the nonlinear wavefront curvature sensing principle. Background Art

[0002] In the field of atmospheric wavefront sensing technology, the sensitivity and dynamic range of the sensing system to the wavefront of light waves passing through atmospheric turbulence are its main performance indicators. In some extreme application fields, such as optical observations of exoplanets, Shack-Hartmann wavefront sensors with large target areas and high subaperture density are usually used. Due to the high manufacturing cost and difficulty of Shack-Hartmann wavefront sensors with large target areas and high subaperture density, people have proposed the idea of ​​inverting the wavefront phase using four off-pupil intensity maps, thereby developing nonlinear wavefront curvature sensing technology.

[0003] According to diffraction theory, the phase information of light waves can be reflected in the defocused intensity distribution. There are many technologies that use the defocused intensity information to sense the phase of light waves. Nonlinear wavefront curvature sensing technology uses four two-by-two symmetrical off-pupil intensity maps to restore the wavefront phase through an iterative algorithm. However, simultaneously acquiring the light intensity distribution at four off-pupil planes increases the complexity of the off-pupil image acquisition system. In addition, because the iterative calculation is time-consuming and easy to fall into the local optimal state, the traditional nonlinear wavefront curvature sensing method has a low sensing efficiency for the actual wavefront.

[0004] In view of the image acquisition problem of nonlinear wavefront curvature sensing, some new optical path solutions can effectively alleviate this situation. For example, a single camera can obtain four off-pupil images in quasi-real time with the help of an electrically controlled zoom lens, but the operating frequency of the electrically controlled zoom lens limits the working efficiency of the overall system. Another solution is to use four cameras to collect off-pupil images in a branching manner. Although it can strictly calculate the wavefront phase in real time, in the scene with limited guide star intensity, the signal-to-noise ratio of each image is seriously reduced due to the dim illumination of each single-channel imaging process. The problems existing in the traditional iterative algorithm GS (Gerchberg-Saxton) can be effectively alleviated by using other iterative algorithms such as ER (Error Reduction) and HIO (Hybrid Input and Output). However, the efficiency of iterative calculation essentially depends on the deviation between the initial value and the true value, so the above method still has the problem of being prone to falling into the local optimal solution.

[0005] It can be seen that the nonlinear wavefront curvature wavefront sensing method has the following disadvantages. In terms of optical path: 1. The operating frequency of the electric zoom system is limited; 2. The branch defocus system has strict requirements on the guide star strength. In terms of algorithm: 1. The iterative calculation speed is slow; 2. The iterative calculation is prone to fall into the local optimal solution. In view of the above problems, it is necessary to find a nonlinear wavefront curvature sensing method that meets the actual application scenarios and needs and has high wavefront calculation efficiency. Summary of the invention

[0006] The present invention proposes a nonlinear wavefront curvature sensing method based on dual off-pupil planes, which can simplify the complexity of the off-pupil image acquisition optical path while improving the wavefront sensing efficiency. An arbitrary off-pupil image acquisition optical path is used to obtain light intensity images at two different off-pupil distances as intensity constraints for the dual-plane GS algorithm. The original four-plane GS algorithm is modified into a dual-plane solution, and the phase solved in the previous frame is used as the initial value for the current frame iteration. Compared with the traditional off-pupil image acquisition optical path, the optical path structure required for obtaining the off-pupil image in the present invention is simple and has good robustness in applicable scenarios. Compared with the traditional wavefront calculation method, the wavefront convergence speed of the present invention is faster and has a lower residual wavefront error, which is suitable for efficiently sensing its wavefront phase in scenarios with limited beam intensity.

[0007] The specific technical solution of the present invention is:

[0008] A nonlinear wavefront curvature sensing method based on a double-pupillary plane, characterized in that the method comprises the following steps:

[0009] S1. According to the scene requirements, select the pupil image acquisition optical path 1 or optical path 2 to obtain two images located at the short pupil position and the long pupil position respectively;

[0010] S2. Write a dual-plane GS algorithm to calculate the light wave phase distribution at the pupil surface (hereinafter referred to as pupil surface phase) through the pupil image;

[0011] S3. Save the pupil phase obtained by each frame and use it as the initial value for the next frame iteration;

[0012] S4. Using two pupil-out matrices as amplitude constraints in the GS loop body, determine whether the phase iterated to the pupil surface meets the convergence condition, and finally obtain the converged pupil surface phase;

[0013] S5. Save the pupil phase obtained in the current frame, collect the next frame of the out-of-pupil image, repeat steps S3 and S4, and calculate the wavefront phase of each frame in time sequence.

[0014] The optical path for collecting the pupil image in step S1 includes: optical path 1 based on an electrically controlled zoom lens, which is suitable for scenes with limited light intensity but low time bandwidth requirements; and optical path 2 based on a branching module, which is suitable for scenes with sufficient light intensity and high time bandwidth requirements.

[0015] The off-pupil image acquisition optical path in step S1 only needs to obtain two off-pupil images, so its structure is simple and easier to implement.

[0016] The order of the double-plane GS iteration surfaces in step S2 is pupil surface, short pupil distance surface, long pupil distance surface and pupil surface.

[0017] The turbulent pupil phases of two frames adjacent in time sequence in step S3 are characterized by having local correlation of spatial distribution.

[0018] The iteration termination condition of step S4 is set to the phase difference between the previous and next two cycles at the pupil plane to improve the solution efficiency of the GS iteration.

[0019] The beneficial effect of the present invention is that a nonlinear wavefront curvature sensing method based on a double-off-pupil plane is proposed for the actual use requirements of nonlinear wavefront curvature sensing. The off-pupil image acquisition system structure based on the method is simple and easy to implement, which alleviates the constraints between light intensity and time bandwidth. Compared with the traditional four-off-pupil plane method, it has a faster computational convergence speed under the same iterative convergence conditions and a lower residual wavefront error under the same iteration time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0021] Figure 1 It is a flow chart of a nonlinear wavefront curvature sensing method based on a double-off-pupil plane in an embodiment of the present invention;

[0022] Figure 2 Schematic diagram of optical path 1 and optical path 2 in an embodiment of the present invention;

[0023] Figure 3 Schematic diagram of the calculation process of the dual-plane GS algorithm in an embodiment of the present invention;

[0024] Figure 3 Middle: Serial numbers ① to ③ are the face order in a single cycle;

[0025] Figure 3 In: R means that the solved phase is used as the initial value of the next frame iteration;

[0026] Figure 4The performances of the conventional nonlinear wavefront curvature sensing method and the nonlinear wavefront curvature sensing method based on the historical frame wavefront in the embodiments of the present invention when the number of the away-pupil planes is changed are shown. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0028] A nonlinear wavefront curvature sensing method based on a double-pupillary plane, characterized in that the method comprises the following steps:

[0029] S1. According to the scene requirements, select the pupil image acquisition optical path 1 or optical path 2 to obtain two images located at the short pupil position and the long pupil position respectively;

[0030] S2. Write a dual-plane GS algorithm to calculate the light wave phase distribution at the pupil surface through the off-pupil image;

[0031] S3. Save the pupil phase obtained by each frame and use it as the initial value for the next frame iteration;

[0032] S4. Using two pupil-out matrices as amplitude constraints in the GS loop body, determine whether the phase iterated to the pupil surface meets the convergence condition, and finally obtain the converged pupil surface phase;

[0033] S5. Save the pupil phase obtained in the current frame, collect the next frame of the out-of-pupil image, repeat steps S3 and S4, and calculate the wavefront phase of each frame in time sequence.

[0034] The optical path for collecting the pupil image in step S1 includes: optical path 1 based on an electrically controlled zoom lens, which is suitable for scenes with limited light intensity but low time bandwidth requirements; and optical path 2 based on a branching module, which is suitable for scenes with sufficient light intensity and high time bandwidth requirements.

[0035] The off-pupil image acquisition optical path in step S1 only needs to obtain two off-pupil images, so its structure is simple and easier to implement.

[0036] The order of the double-plane GS iteration surfaces in step S2 is pupil surface, short pupil distance surface, long pupil distance surface and pupil surface.

[0037] The turbulent pupil phases of two frames adjacent in time sequence in step S3 are characterized by having local correlation of spatial distribution.

[0038] The iteration termination condition of step S4 is set to the phase difference between the previous and next two cycles at the pupil plane to improve the solution efficiency of the GS iteration.

[0039] Embodiment 1: The working process of a nonlinear wavefront curvature sensing method based on a double-pupillary plane is as follows:

[0040] according to Figure 1 As described in step S1 of FIG. 1 , a suitable off-pupil image acquisition optical path is selected according to scene requirements to obtain two images at different off-pupil distances. Figure 2 As shown, optical path 1 is suitable for scenes with limited light intensity but low temporal bandwidth requirements, and optical path 2 is suitable for scenes with sufficient light intensity and high temporal bandwidth requirements.

[0041] according to Figure 1 As described in step S2 in , write a dual-plane GS algorithm. The algorithm calculation process is as follows Figure 3 As shown in the figure, the iterative process is initialized with the pupil phase obtained in the previous frame, and the numerical diffraction calculation of a specific distance from the pupil is performed between each plane in the order of the pupil plane, the short defocus plane, the long defocus plane and the pupil plane. Before the start of numerical diffraction ② and ③, the amplitude matrix in the complex amplitude is replaced by the respective off-pupil intensity matrix. At the end of each cycle, the difference between the pupil phase at this time and the result obtained at the end of the previous cycle is calculated to determine whether it is less than the threshold. If it is less than the threshold, the loop part is continued until the convergence condition is met.

[0042] according to Figure 1 As described in step S3 of the above, the pupil surface phase obtained in the previous frame is used as the iterative initial phase value of the current frame. The pupil image of the current frame obtained by the optical path system specified in step S1 is input into the algorithm written in step S2. Figure 1 As described in step S4 in the above, iterative calculation and convergence condition judgment are started. After the pupil phase of each frame is calculated, it is saved and input into the next frame iteration process.

[0043] The software and hardware devices used in the method involved in the present invention are: Windows 11 operating system, AMD Ryzen 75800H with Radeon Graphics CPU, and MATLAB 2023a simulation platform.

[0044] In order to further verify the actual performance of the nonlinear wavefront curvature sensing method based on double-off-pupil planes involved in the present invention, we compared the differences in perception accuracy and convergence time with the traditional nonlinear wavefront curvature sensing method. Based on the OOMAO adaptive optics toolkit in MATLAB language, the temporal wavefront phase of turbulence fields of different intensities was simulated as a simulation data set. The atmospheric structure is set to three layers, located at 0km, 4km and 10km respectively. The pupil size is set to 2m, and the atmospheric coherence length r0 is taken as 0.5m, 0.25m, 0.125m, 0.0625m and 0.03125m respectively, and the turbulence temporal phase screen with field intensity D / r0 of 4, 8, 16, 32 and 64 is obtained. Experimentally, based on the atmospheric turbulence observation system with a main aperture of 100mm, real atmospheric turbulence data sets with atmospheric coherence length r0 of 7cm, 6cm, 5cm, 4cm and 3cm are obtained. Figure 4 As shown in the figure, the traditional nonlinear wavefront curvature sensing method and the method proposed in the present invention are compared in terms of residual wavefront root mean square and convergence time at 100 frames of the simulated turbulent field D / r0=32, and their performances at different numbers of pupil planes are compared. Figure 4 In the traditional nonlinear wavefront curvature sensing method, the average residual wavefront root mean square error and convergence time in the dual-plane mode are 130.21nm and 3.34s, respectively, which are inferior to the 21.13nm and 1.94s of the four-plane mode. However, in the nonlinear wavefront curvature sensing method based on the historical frame wavefront, the dual-plane mode can maintain a residual wavefront root mean square error comparable to that of the four-plane mode, and its average convergence time is only 0.94s, which is less than 1.95s of the four-plane mode.

[0045] In summary, the present invention relates to a nonlinear wavefront curvature sensing method based on dual pupil planes. When performing wavefront sensing on a turbulent field, high-efficiency work can be achieved by using only two pupil plane intensity information and fewer iteration steps. Compared with the traditional four-pupil plane solution, it has more advantages in terms of wavefront reconstruction accuracy and reconstruction speed. Therefore, the dual pupil plane solution has higher environmental robustness than the traditional four-pupil plane solution in wavefront perception scenarios with high time bandwidth requirements, high precision requirements and limited light intensity.

Claims

1. A nonlinear wavefront curvature sensing method based on a double-pupillary plane, characterized in that The method comprises the following steps: S1. According to the scene requirements, select the pupil image acquisition optical path 1 or optical path 2 to obtain two images located at the short pupil position and the long pupil position respectively; S2. Write a dual-plane GS algorithm to calculate the light wave phase distribution at the pupil surface (hereinafter referred to as pupil surface phase) through the pupil image; S3. Save the pupil phase obtained by each frame and use it as the initial value for the next frame iteration; S4. Using two pupil-out matrices as amplitude constraints in the GS loop body, determine whether the phase iterated to the pupil surface meets the convergence condition, and finally obtain the converged pupil surface phase; S5. Save the pupil phase obtained in the current frame, collect the next frame of the out-of-pupil image, repeat steps S3 and S4, and calculate the wavefront phase of each frame in time sequence.

2. The nonlinear wavefront curvature sensing method based on double-pupillary plane according to claim 1, characterized in that: The optical path for collecting the pupil image in step S1 includes: optical path 1 based on an electrically controlled zoom lens, which is suitable for scenes with limited light intensity but low time bandwidth requirements; and optical path 2 based on a branching module, which is suitable for scenes with sufficient light intensity and high time bandwidth requirements.

3. The nonlinear wavefront curvature sensing method based on double-pupillary plane according to claim 1, characterized in that: The off-pupil image acquisition optical path in step S1 only needs to obtain two off-pupil images, so its structure is simple and easier to implement.

4. The nonlinear wavefront curvature sensing method based on double-pupillary plane according to claim 1, characterized in that: The order of the double-plane GS iteration surfaces in step S2 is pupil surface, short pupil distance surface, long pupil distance surface and pupil surface.

5. The nonlinear wavefront curvature sensing method based on double-pupillary plane according to claim 1, characterized in that: The turbulent pupil phases of two frames adjacent in time sequence in step S3 are characterized by having local correlation of spatial distribution.

6. The nonlinear wavefront curvature sensing method based on double-pupillary plane according to claim 1, characterized in that: The iteration termination condition of step S4 is set to the phase difference between the previous and next two cycles at the pupil plane to improve the solution efficiency of the GS iteration.

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