Mirror convergence method and device based on double feedback surface shape regulation optimization

Abstract

The application provides a mirror convergence method and device based on double feedback surface shape regulation and optimization, which comprises the following steps: monitoring the wavefront component after the surface shape of a deformed mirror is modulated in real time by using a wavefront sensor; judging whether the surface shape coefficient residual of the wavefront component is greater than or equal to a set threshold; if yes, quickly calculating the surface shape reconstruction voltage by using an influence function matrix, and making the deformed mirror quickly converge based on the surface shape reconstruction voltage; if no, monitoring the change of light intensity distribution or imaging definition by using an image sensor, and taking the change as an evaluation parameter of the correction degree of the distorted wavefront; and based on the evaluation parameter, improving the surface shape reconstruction precision by using a search optimization algorithm. The application makes up for the defects that the control precision of the closed-loop control algorithm based on the influence function is affected by the measurement precision of the influence function matrix, and the closed-loop control algorithm without the influence function has low calculation efficiency and is easy to converge to a local optimal value, and can meet the requirements of a non-moving zoom steady image system.

Description

Technical Field

[0001] This invention relates to the field of surface shape control and optimization technology, and particularly to a mirror convergence method and apparatus based on dual-feedback surface shape control and optimization. Background Technology

[0002] Deformable mirrors are important wavefront correctors in the field of adaptive optics. A typical adaptive optics system mainly consists of a wavefront sensor, a wavefront controller, a wavefront corrector, and some auxiliary optical components, which can be found in [reference needed]. Figure 1 As shown in the dashed box, the wavefront sensor detects and displays the aberration composition of the distorted wavefront in real time, and feeds the detection results back to the wavefront controller. The wavefront controller calculates the measured wavefront information and determines the control voltage signal. The wavefront corrector receives the control voltage signal output from the wavefront controller and performs conjugate compensation for the wavefront distortion of the system.

[0003] Piezoelectric stacked deformable mirrors have advantages such as simple mechanical structure, mature research and development technology, and great actuation potential. They are a type of wavefront corrector with high adaptability and mature manufacturing process. The inverse piezoelectric effect of piezoelectric ceramics allows the material to undergo mechanical deformation under the action of an electric field. After applying voltage, each piezoelectric element will produce deformation in the same direction, which will gradually accumulate to the actuator terminal, thereby driving the deformable mirror surface to produce corresponding deformation.

[0004] As a high-precision wavefront controller, the surface shape control accuracy of deformable mirrors is of great significance to the overall performance of the device. Deformable mirrors often have dozens or even thousands of actuation channels and have a large degree of deformation freedom. However, factors such as model error, structural cross-linking coupling and piezoelectric actuation hysteresis can introduce control error. Precise control of the mirror surface shape is a challenge in the application of deformable mirrors.

[0005] Current convergence algorithms for deformable mirrors can be broadly categorized into open-loop control and closed-loop control based on the presence or absence of a closed-loop feedback loop. Closed-loop algorithms can be further divided into those based on influence function theory and those without, depending on whether the influence function matrix needs to be pre-solved for voltage iteration. Open-loop control algorithms require only one surface shape or wavefront measurement, resulting in high solution efficiency and enabling rapid surface shape reconstruction. However, deformable mirrors themselves lack correction capabilities, leading to poor accuracy and robustness. Therefore, using only open-loop algorithms is unsuitable for applications requiring high surface shape accuracy.

[0006] To improve the accuracy of deformable mirror surface reconstruction, voltage iteration can be performed by introducing sensor feedback information. When using wavefront sensors or other sensors that directly sense the wavefront or surface formation components, the surface shape iteration depends on the measurement of the influence function matrix, which is called a closed-loop control algorithm based on influence function theory. This algorithm can improve the robustness of the control process, but the convergence speed is slow, and the construction process of the influence function matrix does not consider the influence of structural cross-linking coupling, resulting in a higher error level. This type of algorithm has a stable iterative convergence process and better control accuracy than open-loop, but the accuracy of the final converged surface shape is greatly affected by the accuracy of the influence function matrix.

[0007] When using image sensors, which indirectly deduce wavefronts or surface components through image processing, the surface shape iteration process does not need to affect the function matrix. This is called a closed-loop control algorithm with no influence function. The accuracy of this type of algorithm is not affected by the error in solving the function matrix. However, it often requires thousands of search optimizations to converge, and there is a possibility of converging to a local optimum, which limits its application range.

[0008] Analysis shows that closed-loop control algorithms based on influence functions have good control efficiency, but their control accuracy is affected by the measurement accuracy of the influence function matrix, and the surface reconstruction accuracy cannot be further improved. Closed-loop control algorithms without influence functions can generally achieve higher surface accuracy, but often require thousands of iterations to converge, and there is a possibility of convergence to a local optimum. Therefore, existing algorithms cannot maintain stable control accuracy under varying aperture and aberration surface conditions, and cannot meet the requirements of stationary zoom stabilization systems. Summary of the Invention

[0009] In view of this, the present invention provides a mirror convergence method and apparatus based on dual-feedback surface shape control optimization to solve at least one of the aforementioned problems.

[0010] To achieve the above objectives, the present invention adopts the following solution:

[0011] According to a first aspect of the present invention, a mirror convergence method based on dual-feedback surface shape modulation optimization is provided. The method includes: using a wavefront sensor to monitor the wavefront components modulated by the surface shape of a deformable mirror in real time; determining whether the surface shape coefficient residual of the wavefront components is greater than or equal to a set threshold; if the surface shape coefficient residual is greater than or equal to the set threshold, then rapidly calculating the surface shape reconstruction voltage through an influence function matrix, and rapidly converging the deformable mirror based on the surface shape reconstruction voltage, and then returning to continue determining whether the surface shape coefficient residual of the wavefront components is greater than or equal to the set threshold; if the surface shape coefficient residual is less than the set threshold, then monitoring changes in light intensity distribution or imaging sharpness using an image sensor as an evaluation parameter for the degree of distortion wavefront correction; based on the evaluation parameter, improving the surface shape reconstruction accuracy through a small-step, high-order search optimization algorithm so that the surface shape reconstruction accuracy reaches the theoretical optimal value of the search optimization algorithm.

[0012] As an embodiment of the present invention, if the residual of the surface shape coefficient is less than the set threshold, the method further includes: after the system has realized the surface shape of the current focal length once, storing the key-value pair mapping relationship of "focal length-channel voltage" in a hash table to quickly realize system zoom.

[0013] As an embodiment of the present invention, if the residual of the surface shape coefficient is less than the set threshold, the method further includes: after the system has realized the surface shape of the current focal length once, storing the key-value pair mapping relationship of "tilt angle-channel voltage" in a hash table to quickly realize system image stabilization.

[0014] As an embodiment of the present invention, before using a wavefront sensor to monitor the wavefront components modulated by the deformable mirror surface shape in real time, the method further includes: determining whether the target focal length or target tilt angle is in a hash table; if it is, directly reading the corresponding channel voltage in the hash table and controlling the deformable mirror based on the channel voltage; if it is not, using a wavefront sensor to monitor the wavefront components modulated by the deformable mirror surface shape in real time.

[0015] As an embodiment of the present invention, the method described above for rapidly obtaining the surface reconstruction voltage through the influence function matrix includes: using the difference between the surface representation vectors of the target surface shape and the current surface shape as an evaluation function, wherein the evaluation function is shown in the following formula:

[0016] J(x) = e n =S d -S n =S d -GC n

[0017] In the above formula, J(x) is the evaluation function, e nS is the deviation of the first four order surface feature vectors between the target surface and the current surface. d C is the surface shape representation vector of the target surface shape. n S is the control voltage signal currently applied to the deformable mirror. n Let G be the current deformed mirror shape representation vector, and G be the influence function matrix.

[0018] Guided by the gradient descent method, the reconstructed voltage signal is adjusted in the opposite direction of the gradient of the evaluation function, resulting in the reconstructed voltage signal of the deformable mirror in the next iteration:

[0019] C n+1 =C n +αG T e n

[0020] In the above formula, C n+1 The voltage signal is reconstructed for the deformable mirror in the next iteration, where α is the gain coefficient.

[0021] As an embodiment of the present invention, the search optimization algorithm in the above method is a stochastic parallel gradient descent algorithm, and the evaluation parameters are:

[0022]

[0023] In the above formula, E is the evaluation parameter, g(x,y) represents the gray value of the image at (x,y), and g(x+1,y) represents the gray value of the image at (x+1,y).

[0024] As an embodiment of the present invention, the wavefront sensor and the image sensor in the above method are implemented using a high-precision interferometer.

[0025] According to a second aspect of the present invention, a mirror convergence device based on dual-feedback surface shape modulation optimization is provided. The device includes: a wavefront monitoring unit for real-time monitoring of wavefront components modulated by a deformable mirror using a wavefront sensor; a judgment unit for judging whether the surface shape coefficient residual of the wavefront components is greater than or equal to a set threshold; a fast reconstruction unit for rapidly calculating a surface shape reconstruction voltage using an influence function matrix when the judgment unit judges that the surface shape coefficient residual is greater than or equal to the set threshold, and rapidly converging the deformable mirror based on the surface shape reconstruction voltage; an image monitoring unit for monitoring changes in light intensity distribution or imaging sharpness using an image sensor as an evaluation parameter for the degree of distortion wavefront correction when the judgment unit judges that the surface shape coefficient residual is less than the set threshold; and a secondary reconstruction unit for improving the surface shape reconstruction accuracy based on the evaluation parameter using a small-step, high-order search optimization algorithm to achieve the theoretical optimal value of the search optimization algorithm.

[0026] As an embodiment of the present invention, the above-mentioned device further includes: a storage unit, used to store the key-value pair mapping relationship of "focal length-channel voltage" in a hash table when the judgment unit determines that the residual of the surface coefficient is less than the set threshold, and after the system has realized the surface of the current focal length once, so as to quickly realize the system zoom.

[0027] As an embodiment of the present invention, the above-mentioned storage unit is further used to store the key-value pair mapping relationship of "tilt angle-channel voltage" in a hash table when the judgment unit determines that the surface shape coefficient residual is less than the set threshold, and after the system has realized the surface shape of the current focal length once, so as to quickly realize the system image stabilization.

[0028] As an embodiment of the present invention, the above-mentioned device further includes: a hash table judgment unit, used to determine whether the target focal length or target tilt angle is in the hash table; if it is, the corresponding channel voltage in the hash table is directly read, and the deformable mirror is controlled based on the channel voltage; if it is not, the wavefront sensor is used to monitor the wavefront component after the deformable mirror surface shape modulation in real time.

[0029] As an embodiment of the present invention, the above-mentioned fast reconstruction unit rapidly obtains the surface reconstruction voltage through the influence function matrix by using the difference between the surface representation vectors of the target surface shape and the current surface shape as an evaluation function, the evaluation function being as follows:

[0030] J(x) = e n =S d -S n =S d -GC n

[0031] In the above formula, J(x) is the evaluation function, e n S is the deviation of the first four order surface feature vectors between the target surface and the current surface. d C is the surface shape representation vector of the target surface shape. n S is the control voltage signal currently applied to the deformable mirror. n Let G be the current deformed mirror shape representation vector, and G be the influence function matrix.

[0032] Guided by the gradient descent method, the reconstructed voltage signal is adjusted in the opposite direction of the gradient of the evaluation function, resulting in the reconstructed voltage signal of the deformable mirror in the next iteration:

[0033] C n+1 =C n +αG T e n

[0034] In the above formula, C n+1 The voltage signal is reconstructed for the deformable mirror in the next iteration, where α is the gain coefficient.

[0035] As an embodiment of the present invention, the above search optimization algorithm is a stochastic parallel gradient descent algorithm, and the evaluation parameters are:

[0036]

[0037] In the above formula, E is the evaluation parameter, g(x,y) represents the gray value of the image at (x,y), and g(x+1,y) represents the gray value of the image at (x+1,y).

[0038] As an embodiment of the present invention, the wavefront sensor and the image sensor in the above-described device are implemented using a high-precision interferometer.

[0039] According to a third aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0040] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0041] As can be seen from the above technical solutions, the mirror convergence method and device based on dual feedback surface shape control optimization provided by the present invention make up for the influence of the measurement accuracy of the influence function matrix on the control accuracy of the closed-loop control algorithm based on the influence function, as well as the defects of the low computational efficiency and easy convergence to local optimum of the closed-loop control algorithm without the influence function. It can maintain the stability of control accuracy under the condition of variable aperture and aberration surface shape, and meet the requirements of stationary zoom image stabilization system. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0043] Figure 1 This is a typical structural diagram of an adaptive optics system;

[0044] Figure 2 This is a flowchart illustrating a mirror convergence method based on dual-feedback surface shape control optimization provided in an embodiment of this application.

[0045] Figure 3 This is a connection diagram of deformable mirror shape control based on ZYGO interferometer provided in an embodiment of this application;

[0046] Figure 4 This is a schematic diagram illustrating the principle of the deformable mirror surface shape optimization control scheme provided in the embodiments of this application;

[0047] Figure 5 This is a schematic flowchart of a mirror convergence method in a stationary zoom image stabilization system provided in an embodiment of this application;

[0048] Figure 6 This is a schematic diagram of the reconstruction error of the variable aperture aberration surface shape by the existing algorithm provided in the embodiments of this application;

[0049] Figure 7 This is a schematic diagram illustrating the reconstruction error of the target surface shape of the variable aperture and variable aberration components by the existing control method provided in the embodiments of this application;

[0050] Figure 8 This is a schematic diagram illustrating the average computational cost of existing algorithms provided in the embodiments of this application;

[0051] Figure 9 This is a schematic diagram of the reconstruction error of the variable aperture aberration surface shape by the dual feedback surface shape control provided in the embodiments of this application;

[0052] Figure 10 This is a schematic diagram of surface reconstruction error statistics provided in an embodiment of this application;

[0053] Figure 11 This is a schematic diagram comparing the average computational cost of DFCS provided in this application embodiment with that of existing algorithms;

[0054] Figure 12 This is a schematic diagram comparing the reconstruction of the surface shape required for zoom stabilization by DFCS and ILC according to embodiments of this application;

[0055] Figure 13 This is a schematic diagram of a mirror convergence device based on dual-feedback surface shape control optimization provided in an embodiment of this application;

[0056] Figure 14 This is a schematic diagram of a mirror convergence device based on dual-feedback surface shape control optimization provided in another embodiment of this application;

[0057] Figure 15 This is a schematic diagram of a mirror convergence device based on dual-feedback surface shape control optimization provided in another embodiment of this application;

[0058] Figure 16 This is a schematic block diagram of the system configuration of the electronic device provided in the embodiments of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0060] like Figure 2 The diagram shown is a schematic flowchart of a mirror convergence method based on dual-feedback surface shape control optimization provided in an embodiment of this application. The method includes the following steps:

[0061] Step S101: Use a wavefront sensor to monitor the wavefront components modulated by the deformable mirror shape in real time.

[0062] In this embodiment, a wavefront sensor is used to monitor the wavefront components modulated by the deformable mirror in real time. It can capture and analyze the phase changes and wavefront distortions of light waves after passing through the deformable mirror. This process is real-time, meaning that the wavefront sensor continuously monitors wavefront changes, ensuring that the system can respond quickly to any wavefront distortions.

[0063] Step S102: Determine whether the surface shape coefficient residual of the wavefront component is greater than or equal to a set threshold. If it is greater than or equal to the set threshold, proceed to step S103. If it is less than the set threshold, proceed to step S104.

[0064] In this embodiment, the surface shape coefficient refers to a parameter used to describe the current surface shape state of the deformable mirror. It can be a Zernike coefficient or other forms of surface shape representation vector. The surface shape coefficient residual refers to the difference between the surface shape representation vectors of the target surface shape and the current mirror surface shape, which can be used to quantify the degree of deviation between the current deformable mirror surface shape and the desired target surface shape. For example, it can be the deviation of the first four order surface shape representation vectors between the target surface shape and the current mirror surface shape.

[0065] Step S103: Quickly obtain the surface reconstruction voltage through the influence function matrix, and make the deformable mirror converge quickly based on the surface reconstruction voltage. Then return to step S102 to continue to determine whether the surface coefficient residual of the wavefront component is greater than or equal to the set threshold.

[0066] The influence function matrix describes the impact of each control channel of the deformable mirror on the surface shape change. Using this matrix, the required control voltage can be calculated to achieve the desired surface shape adjustment. In this embodiment, an iterative algorithm based on influence function theory (such as gradient descent) is used to solve for the reconstruction voltage (channel reconstruction voltage). The principle of gradient descent is that if the evaluation function J(x) is differentiable at point a, then the gradient of J(x) decreases fastest in the opposite direction at a. Through this method, the surface shape of the deformable mirror can quickly converge to a better state. This process ensures that the system can rapidly adjust the surface shape to approach the target surface shape. After rapid surface shape convergence, the system returns to step S102 to continue determining whether the residual of the surface shape coefficients of the wavefront component is greater than or equal to a set threshold. If the residual is still greater than or equal to the threshold, surface shape adjustment continues; otherwise, the system proceeds to the next fine-tuning stage.

[0067] Preferably, the step of "quickly obtaining the surface reconstruction voltage through the influence function matrix" in this step may further include:

[0068] The difference between the surface shape representation vectors of the target surface shape and the current surface shape is used as the evaluation function, which is shown in the following equation:

[0069] J(x) = e n =S d -S n =S d -GC n

[0070] In the above formula, J(x) is the evaluation function, e n S is the deviation of the first four order surface feature vectors between the target surface and the current surface. d C is the surface shape representation vector of the target surface shape. n S is the control voltage signal currently applied to the deformable mirror. n Let G be the current deformed mirror shape representation vector, and G be the influence function matrix.

[0071] Guided by the gradient descent method, the reconstructed voltage signal is adjusted in the opposite direction of the gradient of the evaluation function, resulting in the reconstructed voltage signal of the deformable mirror in the next iteration:

[0072] C n+1 =C n +αG T e n

[0073] In the above formula, C n+1 The voltage signal is reconstructed for the deformable mirror in the next iteration, where α is the gain coefficient.

[0074] Step S104: Use an image sensor to monitor changes in light intensity distribution or image sharpness as an evaluation parameter for the degree of distortion wavefront correction.

[0075] By monitoring changes in light intensity distribution or image sharpness, the image sensor can provide a quantitative assessment of the current wavefront correction level. This information can be used to determine the effectiveness of wavefront distortion correction. The imaging data acquired by the image sensor is fed back to the deformable mirror's surface shape control system. Through this feedback, the system can further adjust the actuation voltage to optimize the surface shape reconstruction accuracy.

[0076] Step S105: Based on the evaluation parameters, improve the surface reconstruction accuracy by using a search optimization algorithm with small step size and high number of iterations, so that the surface reconstruction accuracy reaches the theoretical optimal value of the search optimization algorithm.

[0077] As can be seen from the aforementioned steps, the search optimization algorithm will only be used to perform further search optimization when the residual of the surface shape coefficient is less than the set threshold, based on the feedback from the image sensor.

[0078] The surface shape coefficient residual is used as the deviation e of the first four order surface shape representation vectors between the target surface shape and the current mirror surface shape. n For example, when its e n When the voltage is less than the threshold t, further search and optimization can be performed using a voltage search algorithm (such as the Stochastic Parallel Gradient Descent algorithm, SPGD) to approximate and stabilize at the theoretical optimal value. The entire control process of steps S101-S105 above can be represented by the following formula:

[0079]

[0080] In the above formula, e n The case where t is greater than or equal to t represents the processing in step S103, while e n The case where the value is less than t represents the processing in steps S104 and S105. Here, E is the evaluation parameter for the SPGD search phase, and the energy gradient function is used, its expression being:

[0081]

[0082] In the above formula, E is the evaluation parameter, g(x,y) represents the gray value of the image at (x,y), and g(x+1,y) represents the gray value of the image at (x+1,y).

[0083] Preferably, when the residual of the surface shape coefficient is determined to be less than the set threshold in step S102, the method of this application may further include: after the system has implemented the surface shape of the current focal length once, storing the key-value pair mapping relationship of "focal length-channel voltage" in a hash table to quickly realize system zoom.

[0084] After the system successfully adjusts the aspect ratio for the current focal length, the key-value mapping relationship between "focal length and channel voltage" is stored in a hash table. This process ensures that when the same focal length needs to be reproduced in the future, the calculated channel voltage can be quickly read from the hash table and applied, thus achieving fast zooming. Therefore, this method reduces the need to recalculate the voltage for each zoom, improving the system's response speed and efficiency.

[0085] Further preferably, when the residual of the surface shape coefficient is determined to be less than the set threshold in step S102, the method of this application may further include: after the system has realized the surface shape of the current focal length once, storing the key-value pair mapping relationship of "tilt angle-channel voltage" in a hash table to quickly realize system image stabilization.

[0086] Further preferably, after storing the aforementioned "focal length-channel voltage" and "tilt angle-channel voltage" using a hash table, and before using a wavefront sensor to monitor the wavefront components modulated by the deformable mirror surface shape in real time, the method of this application may further include: determining whether the target focal length or target tilt angle is in the hash table; if it is, directly reading the corresponding channel voltage from the hash table and controlling the deformable mirror based on the channel voltage; if it is not, using a wavefront sensor to monitor the wavefront components modulated by the deformable mirror surface shape in real time.

[0087] This approach improves system efficiency and responsiveness by prioritizing the use of pre-stored data in a hash table, reducing the need for real-time computation. Simultaneously, it retains the ability to monitor in real-time to handle unstored focal lengths or tilt angles, ensuring system flexibility and accuracy.

[0088] Preferably, the functions of the wavefront sensor and the image sensor in the above steps can be implemented using a high-precision interferometer. This is because, in addition to acquiring the standard Zernike coefficients of the deformable mirror surface shape, thus functioning as a wavefront sensor, the high-precision interferometer can also directly acquire the sag information of the current surface shape as an evaluation parameter, replacing the function of the image sensor. The dual-feedback surface shape control method for the deformable mirror using a high-precision interferometer can be expressed as follows: Figure 3 As shown, the solid black line represents the hardware data link. Initially, the interferometer feeds back the measured standard Zernike coefficients of the surface shape to the computer, which then iterates the voltage based on the influence function matrix. When the voltage falls below the conversion threshold, the interferometer feeds back the collected surface shape elevation data to the computer. Using the RMSE between the measured elevation and the target elevation as an evaluation parameter, a voltage refinement search begins, thus achieving dual-feedback surface shape control based on a high-precision interferometer deformable mirror.

[0089] Replacing the wavefront sensor and image sensor with a high-precision interferometer simplifies the system's hardware configuration. Furthermore, the interferometer provides high-precision surface shape measurement capabilities, accurately capturing subtle changes in the surface shape. This high-precision measurement is crucial for achieving accurate surface shape reconstruction and wavefront correction. By directly acquiring the surface shape's sag information, the interferometer can provide more accurate evaluation parameters for feedback control, which helps improve the accuracy of surface shape reconstruction, bringing it closer to the global optimum. Therefore, using a high-precision interferometer not only simplifies the system structure but also significantly improves the accuracy and efficiency of surface shape control, meeting the high requirements of stationary zoom-stabilized image systems.

[0090] The principle of the mirror convergence method based on dual-feedback surface shape control optimization described in steps S101-S105 above can also be found in [reference missing]. Figure 4 As shown, the object light emitted (reflected) by a target at infinity can be approximated as parallel light. Due to atmospheric turbulence and optical elements, a certain degree of wavefront distortion will occur. The wavefront sensor (WFS) monitors the wavefront components after surface shape modulation by the deformable mirror in real time and feeds back the residual wavefront shape composition coefficients to the deformable mirror. The image sensor (CMOS) detects the image of the target after wavefront distortion compensation in real time and also feeds back the image quality parameters to the surface shape control of the deformable mirror, forming a dual-feedback surface shape control method.

[0091] The above method will be further explained below using a stationary zoom image stabilization system as an example. Figure 5 The diagram shown is a schematic flowchart of a mirror convergence method in a stationary zoom image stabilization system according to an embodiment of the present invention. Figure 5 As can be seen, after zoom stabilization begins, the system first determines whether the target focal length or tilt angle is in the hash table. If it is, the voltage in the hash table is read, the deformable mirror (DM) generates the corresponding deformation, and the image sensor acquires the image. If not, the wavefront sensor detects the deformable mirror shape in real time and checks whether the 2-norm of the residuals of the first four shape coefficients is less than a threshold. If it is less than the threshold, the image sensor acquires the image, storing the "focal length-channel voltage" and "tilt angle-channel voltage" in the hash table. If they are not less than the threshold, the iterative voltage is calculated, and the DM is controlled to generate the corresponding deformation. After entering the image sensor acquisition step, the system checks whether the evaluation parameters are stable. If stable, zoom stabilization ends; if unstable, bidirectional voltage random perturbations are generated, executed separately by the DM, and then gradient estimation is performed to update the control voltage.

[0092] As can be seen from the above, the mirror convergence method based on dual feedback surface shape control optimization provided by the present invention makes up for the influence of the measurement accuracy of the influence function matrix on the control accuracy of the closed-loop control algorithm based on the influence function, as well as the defects of the closed-loop control algorithm without the influence function, such as low computational efficiency and easy convergence to local optimum. It can maintain the stability of control accuracy under the condition of variable aperture and aberration surface shape, and meet the requirements of stationary zoom image stabilization system.

[0093] The following section uses specific experimental data, taking a stationary zoom image stabilization system as an example, to further describe the effectiveness of the method described above in this application:

[0094] In stationary zoom stabilization systems, the aperture and target surface composition vary across different focal lengths. To verify whether existing surface shape control algorithms can maintain stable control accuracy under varying aperture conditions, pseudo-inverse, SD, ILC, and SPGD simulations were used to control a deformable mirror to achieve the first six Zenick aberration terms of the surface shape under varying aperture conditions. The root mean square error (RMSE) of the surface shape reconstruction results was recorded and used as an evaluation standard for the deformable mirror's fitting accuracy to the freeform surface; a smaller RMSSE indicates a more accurate fit to the mirror surface shape. Using 30% to 80% of the mirror aperture as the working aperture of the deformable mirror, and varying the aperture from small to large with a step frequency of 2%, the statistical results of the reconstruction errors of the first six Zenick aberration terms of the surface shape under different aperture conditions are as follows: Figure 6 As shown in the figure, the shades of gray represent the root mean square error of the algorithm in reconstructing the target surface shape; the darker the color, the larger the root mean square error.

[0095] Depend on Figure 6 Simulation results show that the open-loop pseudo-inverse method has a large error in controlling the surface shape of small aperture and high order, which is not suitable for high-precision applications such as imaging systems. The reconstruction accuracy of the SD and ILC methods is relatively close overall, indicating that they are essentially similar algorithms. Their reconstruction accuracy is affected by the accuracy of the function matrix and cannot be further improved. SPGD has poor reconstruction accuracy for defocus (Z5) and spherical aberration (Z13) surfaces. Since the freeform surfaces that deformable mirrors in stationary zoom systems need to achieve are often mainly composed of defocus surfaces, SPGD is not suitable for use in zoom image stabilization systems.

[0096] statistics Figure 6 The average value of the reconstruction error against the Zernike term number and the target aperture represents the algorithm's adaptability to aberration surface shape and variable aperture, respectively. The results are as follows: Figure 7 As shown.

[0097] Depend on Figure 7It can be seen that as the order of the target surface shape increases, the surface shape reconstruction error of existing algorithms shows an upward trend. Among them, the open-loop pseudo-inverse method has the largest error when realizing complex surface shapes, and the SPGD method has a significantly increased error when realizing defocused and spherical aberration surface shapes. When the aperture changes, the solution error of the pseudo-inverse method, SD, and ILC first decreases, then increases, and then decreases again with the increase of the aperture. Among them, the pseudo-inverse method has a larger error when realizing surface shapes with small apertures. The surface shape realization accuracy of SPGD is worse than the pseudo-inverse method but worse than SD and ILC when the aperture of the target surface shape is small. The realization accuracy is the worst among existing algorithms when the aperture is medium, and the best when the aperture is large. Comprehensive analysis shows that none of the above algorithms can meet the requirements of stationary zoom stabilization systems for the surface shape reconstruction accuracy of target surfaces with varying aperture and aberration components.

[0098] Using the computational cost of the open-loop pseudo-inverse method as a unit, the computational efficiency of existing algorithms is compared, and the statistical results are as follows: Figure 8 As shown, the SD and ILC methods are more computationally efficient, while SPGD is the least efficient.

[0099] The above analysis shows that closed-loop control algorithms based on influence functions have good control efficiency, but their control accuracy is affected by the measurement accuracy of the influence function matrix, and the surface reconstruction accuracy cannot be further improved. Closed-loop control algorithms without influence functions can generally achieve higher surface accuracy, but often require thousands of iterations to converge, and there is a possibility of convergence to a local optimum. Existing algorithms cannot maintain stable control accuracy under varying aperture and aberration surface conditions, failing to meet the requirements of stationary zoom stabilization systems.

[0100] After implementing the mirror convergence method based on dual-feedback surface shape control optimization, in order to investigate the implementation accuracy of the dual-feedback surface shape control method when changing the aperture and aberration surface shape, the root mean square error of surface shape reconstruction was also recorded as follows: Figure 9 As shown, DFSC is the mirror convergence method based on dual-feedback surface shape control optimization proposed in this application.

[0101] The average root mean square error (RMSE) of the reconstruction of surface shapes with different aberration components at the same aperture was statistically analyzed using the dual-feedback surface shape control method, and the average RMSE of the reconstruction of surface shapes with the same aberration components at different apertures was compared with existing methods. The statistical results are as follows: Figure 10 As shown.

[0102] It can be seen that, compared with existing algorithms, the control accuracy of the dual feedback surface shape control method proposed in this patent is the best under the full range of light-transmitting aperture and the first six aberration surface shapes. In particular, the reconstruction accuracy has been significantly improved in the realization of defocus surface shape, which proves the practicality of the algorithm used in this patent. It can adjust the optical power of the mirror with high precision and meet the requirements of constructing an offline control voltage hash set for a stationary zoom system.

[0103] Using the computation time of the pseudo-inverse method as a benchmark, the computational costs of other existing algorithms are standardized and compared with those of the method in this patent. The statistical results are as follows: Figure 11 As shown. The results indicate that the computational cost of this patented method is reduced by approximately 43% compared to SPGD, and is 6-8 times the computational cost of SD or ILC.

[0104] Achieving defocused surface shapes with different actuation amounts is the foundation of the system's zoom capability, while achieving various freeform aberration surface shapes is the foundation of the system's image stabilization and image quality optimization. Using a dual-feedback surface shape control method and an iterative learning control method, we simulated and reconstructed defocused surface shapes with different actuation amounts and different aberration surface shapes with the same actuation amount, and statistically analyzed their root mean square errors. The results are as follows: Figure 12 As shown.

[0105] The results show that as the defocus term actuation amount increases, the surface reconstruction error of both algorithms increases, but the error accumulation rate of ILC is faster. The DFCS method proposed in this patent can reduce the root mean square error of reconstruction by more than 40%. Simulation reconstruction of freeform surfaces such as third and fifth order aberrations, coma, first and higher order spherical aberrations, and cloverleaf patterns commonly found in optical systems was performed. The actuation amount of the preset target surface was 8 micrometers. The simulation results show that the method of this patent can also significantly reduce the root mean square error of reconstruction of freeform surfaces by deformable mirrors by more than 50%, which is beneficial to the realization of freeform surfaces in zoom stabilization systems.

[0106] When the stationary zoom system uses a hash set to pre-store the "focal length-voltage" key-value pairs, the lookup time complexity is only O(1), which is negligible. The zoom response delay is only related to the actuator response delay. The approximate momentum deformable mirror actuator used in the experimental verification of the surface shape control optimization algorithm of this patent is model PPA80L, with capacitor C. piezo =5.4μF, the drive power supply is CILAS LA75B, and its maximum output current is I. lim =360mA. When the actuator is operating dynamically, the current and signal frequency have an approximately linear relationship, as shown in the following formula:

[0107] i≈2πfC piezo V P

[0108] In the formula V P To control the midpoint of the voltage range, for PPA80L, V P =85V. Therefore, the maximum operating frequency of the system can be expressed by the following formula:

[0109]

[0110] Will I lim V P C piezoSubstituting the value into the above formula, we can obtain that the maximum operating frequency is approximately 125Hz. Therefore, the time required for this actuator to achieve full-stroke actuation is t = 1 / f. max =8ms, which meets the millisecond-level zoom requirements of stationary zoom systems.

[0111] The experimental data above clearly show that the mirror convergence method based on dual feedback surface shape control optimization proposed in this application makes up for the influence of the measurement accuracy of the influence function matrix on the control accuracy of the closed-loop control algorithm based on the influence function, as well as the defects of the closed-loop control algorithm without the influence function, such as low computational efficiency and easy convergence to local optima. It can maintain stable control accuracy under the condition of variable aperture and aberration surface shape, and meet the requirements of stationary zoom image stabilization system.

[0112] like Figure 13 The diagram shown is a schematic representation of a mirror convergence device based on dual-feedback surface shape control optimization according to an embodiment of this application. The device includes: a wavefront monitoring unit 130, a judgment unit 131, a fast reconstruction unit 132, an image monitoring unit 133, and a secondary reconstruction unit 134. The judgment unit 131 is connected to the wavefront monitoring unit 130, the fast reconstruction unit 132, and the image monitoring unit 133, respectively. The secondary reconstruction unit 134 is connected to the image monitoring unit 133.

[0113] The wavefront monitoring unit 130 is used to monitor the wavefront components modulated by the deformable mirror shape in real time using a wavefront sensor.

[0114] The judgment unit 131 is used to determine whether the surface shape coefficient residual of the wavefront component is greater than or equal to a set threshold.

[0115] The fast reconstruction unit 132 is used to quickly obtain the surface reconstruction voltage through the influence function matrix when the judgment unit determines that the surface coefficient residual is greater than or equal to the set threshold, and to make the deformable mirror converge quickly based on the surface reconstruction voltage.

[0116] The image monitoring unit 133 is used to monitor changes in light intensity distribution or imaging sharpness using an image sensor when the judgment unit determines that the surface coefficient residual is less than the set threshold, as an evaluation parameter for the degree of distortion wavefront correction.

[0117] The secondary reconstruction unit 134 is used to improve the surface reconstruction accuracy based on the evaluation parameters by using a search optimization algorithm with small step size and high degree of search to make the surface reconstruction accuracy reach the theoretical optimal value of the search optimization algorithm.

[0118] Preferred, such as Figure 14As shown, the above-mentioned device further includes: a storage unit 135, used to store the key-value pair mapping relationship of "focal length-channel voltage" in a hash table when the judgment unit determines that the residual of the surface coefficient is less than the set threshold, and after the system has realized the surface of the current focal length once, so as to quickly realize the system zoom.

[0119] Preferably, the storage unit 135 is further configured to store the key-value pair mapping relationship of "tilt angle-channel voltage" in a hash table when the judgment unit determines that the residual of the surface shape coefficient is less than the set threshold, and after the system has realized the surface shape of the current focal length once, so as to quickly realize the system image stabilization.

[0120] Preferred, such as Figure 15 As shown, the above-mentioned device further includes: a hash table judgment unit 136, used to determine whether the target focal length or target tilt angle is in the hash table. If it is, the corresponding channel voltage in the hash table is read directly, and the deformable mirror is controlled based on the channel voltage. If it is not, the wavefront sensor is used to monitor the wavefront component after the deformable mirror surface shape is modulated in real time.

[0121] Preferably, the above-mentioned fast reconstruction unit 132 rapidly obtains the surface reconstruction voltage through the influence function matrix by using the difference between the surface representation vectors of the target surface shape and the current surface shape as an evaluation function, the evaluation function being as follows:

[0122] J(x) = e n =S d -S n =S d -GC n

[0123] In the above formula, J(x) is the evaluation function, e n S is the deviation of the first four order surface feature vectors between the target surface and the current surface. d C is the surface shape representation vector of the target surface shape. n S is the control voltage signal currently applied to the deformable mirror. n Let G be the current deformed mirror shape representation vector, and G be the influence function matrix.

[0124] Guided by the gradient descent method, the reconstructed voltage signal is adjusted in the opposite direction of the gradient of the evaluation function, resulting in the reconstructed voltage signal of the deformable mirror in the next iteration:

[0125] C n+1 =C n +αG T e n

[0126] In the above formula, C n+1 The voltage signal is reconstructed for the deformable mirror in the next iteration, where α is the gain coefficient.

[0127] Preferably, the above search optimization algorithm is a stochastic parallel gradient descent algorithm, and the evaluation parameters are:

[0128]

[0129] In the above formula, E is the evaluation parameter, g(x,y) represents the gray value of the image at (x,y), and g(x+1,y) represents the gray value of the image at (x+1,y).

[0130] Preferably, the wavefront sensor and the image sensor in the above device are implemented using a high-precision interferometer.

[0131] For a detailed description of each of the above units, please refer to the corresponding descriptions in the foregoing method embodiments, which will not be repeated here.

[0132] As can be seen from the above technical solution, the mirror convergence device based on dual feedback surface shape control optimization provided by the present invention makes up for the influence of the measurement accuracy of the influence function matrix on the control accuracy of the closed-loop control algorithm based on the influence function, as well as the defects of the closed-loop control algorithm without the influence function, such as low computational efficiency and easy convergence to local optimum. It can maintain the stability of control accuracy under the condition of variable aperture and aberration surface shape, and meet the requirements of stationary zoom image stabilization system.

[0133] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0134] This invention also provides a computer-readable storage medium storing a computer program for performing the above-described methods.

[0135] like Figure 16 As shown, the electronic device 600 may also include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily need to include these components. Figure 16 All components shown; in addition, the electronic device 600 may also include Figure 16 For components not shown, please refer to existing technologies.

[0136] like Figure 16 As shown, the central processing unit 100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device. The central processing unit 100 receives inputs and controls the operation of various components of the electronic device 600.

[0137] The memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 100 may execute the program stored in the memory 140 to perform information storage or processing, etc.

[0138] Input unit 120 provides input to central processing unit 100. Input unit 120 may be, for example, a keypad or touch input device. Power supply 170 provides power to electronic device 600. Display 160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0139] The memory 140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 140 can also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142 for storing application programs and function programs or processes for executing the operation of the electronic device 600 via the central processing unit 100.

[0140] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0141] The communication module 110 is a transmitter / receiver that transmits and receives signals via the antenna 111. The communication module 110 (transmitter / receiver) is coupled to the central processing unit 100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0142] Based on different communication technologies, multiple communication modules 110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 110 (transmitter / receiver) is also coupled to a speaker 1301 and a microphone 1302 via an audio processor 1300 to provide audio output via the speaker 1301 and receive audio input from the microphone 1302, thereby realizing typical telecommunications functions. The audio processor 1300 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 1300 is also coupled to a central processing unit 100, enabling on-device recording via the microphone 1302 and on-device playback of stored audio via the speaker 1301.

[0143] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. 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. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0145] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0147] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A mirror convergence method based on double feedback surface shape regulation optimization, characterized in that, The method comprises: real-time monitoring of the wavefront component after the deformation mirror surface shape modulation by using a wavefront sensor; judging whether the surface shape coefficient residual of the wavefront component is greater than or equal to a set threshold value; if the surface shape coefficient residual is greater than or equal to the set threshold value, quickly calculating the surface shape reconstruction voltage by using an influence function matrix, and making the deformation mirror quickly converge based on the surface shape reconstruction voltage, and then returning to continue judging whether the surface shape coefficient residual of the wavefront component is greater than or equal to a set threshold value; if the surface shape coefficient residual is less than the set threshold value, using an image sensor to monitor the change of light intensity distribution or imaging clarity as an evaluation parameter of the distortion wavefront correction degree; based on the evaluation parameter, using a small-step and high-order search optimization algorithm to improve the surface shape reconstruction precision to the theoretical optimal value of the search optimization algorithm; if the surface shape coefficient residual is less than the set threshold value, the method further comprises: after the system realizes the surface shape of the current focal segment, storing the key-value pair mapping relationship of "focal segment-channel voltage" in a hash table to quickly realize system zooming, and storing the key-value pair mapping relationship of "tilt angle-channel voltage" in a hash table to quickly realize system image stabilization; before real-time monitoring of the wavefront component after the deformation mirror surface shape modulation by using a wavefront sensor, the method further comprises: judging whether the target focal segment or the target tilt angle is in the hash table, if yes, directly reading the corresponding channel voltage in the hash table, and controlling the deformation mirror based on the channel voltage; if not, real-time monitoring of the wavefront component after the deformation mirror surface shape modulation by using a wavefront sensor.

2. The mirror convergence method based on dual feedback face shape regulation optimization according to claim 1, wherein, the quick calculation of the surface shape reconstruction voltage by using the influence function matrix comprises: using the difference between the target surface shape and the current surface shape as the surface shape characterization vector as an evaluation function, the evaluation function is as follows: J(x) = e n = S d - S n = S d - GC n In the above equation, J(x) is the merit function, e n is the deviation of the first four orders of the surface representation vectors of the target surface shape and the current surface shape, S d is the surface representation vector of the target surface shape, C n is the control voltage signal currently acting on the deformable mirror, S n is the surface representation vector of the current deformable mirror, G is the influence function matrix; under the guidance of the gradient descent method, adjusting the reconstruction voltage signal along the gradient direction of the evaluation function, and the deformation mirror reconstruction voltage signal of the next iteration: C n+1 = C n + aG T e n C in the above equation n+1 for the next iteration of the deformable mirror reconstruction voltage signal, and a is a gain factor.

3. The mirror convergence method based on dual feedback face shape regulation optimization of claim 1, wherein, the search optimization algorithm is a random parallel gradient descent algorithm, and the evaluation parameter is: E = ∑ y ∑ x (|g(x+1,y)-g(x,y)| 2 +|g(x,y+1)-g(x,y)| 2 ) in the above formula, E is the evaluation parameter, g(x, y) represents the gray value of the image at (x, y), and g(x+1, y) represents the gray value of the image at (x+1, y).

4. The mirror convergence method based on dual feedback face shape regulation optimization of claim 1, wherein, the wavefront sensor and the image sensor are realized by using a high-precision interferometer.

5. A mirror converging device based on double feedback surface shape regulation optimization, characterized in that, The device comprises: a wavefront monitoring unit for real-time monitoring of the wavefront component after the deformation mirror surface shape modulation by using a wave front sensor; a judging unit for judging whether the surface shape coefficient residual of the wavefront component is greater than or equal to a threshold value; a quick reconstruction unit for quickly calculating the surface shape reconstruction voltage by using an influence function matrix when the judging unit judges that the surface shape coefficient residual is greater than or equal to the set threshold value, and making the deformation mirror quickly converge based on the surface shape reconstruction voltage; an image monitoring unit for using an image sensor to monitor the change of light intensity distribution or imaging clarity as an evaluation parameter for the distortion wavefront correction degree when the judging unit judges that the surface shape coefficient residual is less than the set threshold value. a secondary reconstruction unit, configured to improve the surface shape reconstruction precision based on the evaluation parameter by a search optimization algorithm with small step size and high order, so that the surface shape reconstruction precision reaches a theoretical optimal value of the search optimization algorithm; a storage unit, configured to store a key-value mapping relationship between "focal length-channel voltage" in a hash table when the judgment unit judges that the surface shape coefficient residual error is less than the set threshold value and after the system has implemented the surface shape of the current focal length once, so as to quickly realize system zooming, and to store a key-value mapping relationship between "tilt angle-channel voltage" in a hash table when the judgment unit judges that the surface shape coefficient residual error is less than the set threshold value and after the system has implemented the surface shape of the current focal length once, so as to quickly realize system image stabilization; a hash table judgment unit, configured to judge whether a target focal length or a target tilt angle is in the hash table, if yes, directly read the corresponding channel voltage in the hash table, and control the deformable mirror based on the channel voltage; if not, utilize the wavefront sensor to monitor the wavefront component modulated by the deformable mirror in real time.

6. The dual feedback surface shape regulation optimized mirror convergence device of claim 5, wherein, the fast reconstruction unit quickly calculates the surface shape reconstruction voltage by an influence function matrix, and the influence function matrix is calculated by the following formula: take the difference between the target surface shape and the current surface shape as the evaluation function, and the evaluation function is as follows: J(x) = e n = S d - S n = S d - GC n In the above equation, J(x) is the merit function, e n is the deviation of the first four order surface shape representation vectors of the target surface shape and the current surface shape, S d is the surface shape representation vector of the target surface shape, C n is the current control voltage signal acting on the deformable mirror, S n is the current surface shape representation vector of the deformable mirror, G is the influence function matrix; under the guidance of the gradient descent method, adjust the reconstruction voltage signal along the gradient direction of the evaluation function, and the deformable mirror reconstruction voltage signal of the next iteration is: C n+1 = C n + aG T e n C in the above equation n+1 for the next iteration of the deformable mirror reconstruction voltage signal, and a is a gain factor.

7. The dual feedback surface shape regulation optimized mirror convergence device of claim 5, wherein, the search optimization algorithm is a random parallel gradient descent algorithm, and the evaluation parameter is: E = ∑ y ∑ x (|g(x+1,y)-g(x,y)| 2 +|g(x,y+1)-g(x,y)| 2 ) in the above formula, E is the evaluation parameter, g(x, y) represents the gray value of the image at (x, y), and g(x+1, y) represents the gray value of the image at (x+1, y).

8. The dual feedback surface shape regulation optimized mirror convergence device of claim 5, wherein, the wavefront sensor and the image sensor are implemented by using a high-precision interferometer.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, the processor executes the computer program to realize the steps of the method in any one of claims 1 to 4.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, the computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 4.

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