Wavefront Sensorless Adaptive Optics System Research Articles

Hybrid control method for generating airy beam in wavefront sensorless adaptive optics system

In order to reduce the complexity of Airy beams generation based on adaptive optics with a wavefront sensor, a wavefront sensorless adaptive optics system, which employs a camera to measure the far-field intensity distribution as the feedback for optimizing the control voltages of the deformable mirror, is proposed for generating high-quality Airy beams. A hybrid control method combining hill-climbing algorithm and open-loop control method is developed to correct the aberrations of the system and produce the cubic phases. High-quality tunable Airy beams were generated successfully. The properties of the generated Airy beams, such as non-diffraction, self-acceleration and self-healing, were verified. The experimental results coincide with the theoretical results, showing the proposed method has potential applications such as Airy beam light-sheet microscope and optical manipulation.

A Novel SPGD Algorithm for Wavefront Sensorless Adaptive Optics System

Stochastic parallel gradient descent (SPGD) is the most frequently used optimization algorithm for correcting wavefront distortion in the wavefront sensorless adaptive optics(WFS-Less AO)system. However, the convergence speed of the SPGD algorithm becomes slow rapidly as increasing of distortion, and the probability of falling into local optimum is rising owing to the fixed gain coefficient. It cannot meet the requirement of real-time wavefront distortion correction. Therefore, a novel algorithm is proposed in this paper, called as adaptive gain stochastic parallel gradient descent (AGSPGD) based on the AMSGrad optimizer in the deep learning, to improve the convergence speed of the algorithm and to reduce the probability of falling into local optimum. The AGSPGD algorithm adopts the first-order moment and the second-order moment of the performance index, which are combined to dynamically adjust the gain. The numerical simulations are completed in this paper. The results of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$D/r_0=2.5$</tex-math></inline-formula> conditions demonstrate that the AGSPGD can reduce the number of iterations by 25%, and the probability of the algorithm falling into local optimum is reduced from 16% to 4%. In addition, the AGSPGD still outperforms the SPGD as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$D/r_0$</tex-math></inline-formula> increasing.

Adaptive Optical Closed-Loop Control on the Basis of Hyperparametric Optimization of Convolutional Neural Networks

In adaptive optics systems, the precision wavefront sensor determines the closed-loop correction effect. The accuracy of the wavefront sensor is severely reduced when light energy is weak, while the real-time performance of wavefront sensorless adaptive optics systems based on iterative algorithms is poor. The wavefront correction algorithm based on deep learning can directly obtain the aberration or correction voltage from the input image light intensity data with better real-time performance. Nevertheless, manually designing deep-learning models requires a multitude of repeated experiments to adjust many hyperparameters and increase the accuracy of the system. A wavefront sensorless system based on convolutional neural networks with automatic hyperparameter optimization was proposed to address the aforementioned issues, and networks known for their superior performance, such as ResNet and DenseNet, were constructed as constructed groups. The accuracy of the model was improved by over 26%, and there were fewer parameters in the proposed method, which was more accurate and efficient according to numerical simulations and experimental validation.

CoolMomentum-SPGD Algorithm for Wavefront Sensor-Less Adaptive Optics Systems

Instead of acquiring the previous aberrations of an optical wavefront with a sensor, wavefront sensor-less (WFSless) adaptive optics (AO) systems compensate for wavefront distortion by optimizing the performance metric directly. The stochastic parallel gradient descent (SPGD) algorithm is pervasively adopted to achieve performance metric optimization. In this work, we incorporate CoolMomentum, a method for stochastic optimization by Langevin dynamics with simulated annealing, into SPGD. Numerical simulations reveal that, compared with the state-of-the-art SPGD variant, the proposed CoolMomentum-SPGD algorithm achieves better convergence speed under various atmospheric turbulence conditions while requiring only two tunable parameters.

Modified Gerchberg-Saxton algorithm-based probe-free wavefront distortion compensation of an OAM beam

In the free space optical communication system, atmospheric turbulence (AT) will distort the helical phase of the orbital angular momentum (OAM) beam, resulting in inter-mode crosstalk and system performance degradation. Based on modified Gerchberg-Saxton (GS) algorithm, this paper proposes a wavefront sensorless adaptive optics system to compensate the wavefront distortion of an OAM beam. For the modified GS algorithm, at each iteration, the spatial phase is re-modulated to descend along a new phase gradient direction to improve algorithm convergence. At the same time, in the spatial domain, negative feedback is generated by nonlinear operation to optimize the beam amplitude, so as to speed up the convergence speed of the algorithm. Simulation results show that our modified GS algorithm can better compensate the wavefront distortion of an OAM beam caused by atmospheric turbulence than the traditional GS and hybrid input–output algorithms, which provides a new idea to solve the problem of atmospheric turbulence in free space optical communication system.

The Lattice Geometry of Walsh-Function-Based Adaptive Optics

We show that there is an intrinsic link between the use of Walsh aberration modes in adaptive optics (AO) and the mathematics of lattices. The discrete and binary nature of these modes means that there are infinite combinations of Walsh mode coefficients that can optimally correct the same aberration. Finding such a correction is hence a poorly conditioned optimisation problem that can be difficult to solve. This can be mitigated by confining the AO correction space defined in Walsh mode coefficients to the fundamental Voronoi cell of a lattice. By restricting the correction space in this way, one can ensure there is only one set of Walsh coefficients that corresponds to the optimum correction aberration. This property is used to enable the design of efficient estimation algorithms to solve the inverse problem of finding correction aberrations from a sequence of measurements in a wavefront sensorless AO system. The benefit of this approach is illustrated using a neural-network-based estimator.

Nesterov-accelerated adaptive momentum estimation-based wavefront distortion correction algorithm

In order to improve the wavefront distortion correction performance of the classical stochastic parallel gradient descent (SPGD) algorithm, an optimized algorithm based on Nesterov-accelerated adaptive momentum estimation is proposed. It adopts a modified second-order momentum and a linearly varying gain coefficient to improve iterative stability. It integrates the Nesterov momentum term and the modified Adam optimizer to further improve the convergence speed, correct the direction of gradient descent in a timely fashion, and avoid falling into local extremum. Besides, to demonstrate the algorithm's performance, a wavefront sensorless adaptive optics system model is established using a 6×6 element deformable mirror as wavefront corrector. Simulation results show that, compared with the SPGD algorithm, the proposed algorithm converges faster, and its Strehl ratio after convergence is nearly 6.25 times that of the SPGD algorithm. Also, the effectiveness and superiority of the proposed algorithm are verified by comparing with two existing optimization algorithms.

Wavefront Sensorless Aberration Correction With Magnetic Fluid Deformable Mirror for Laser Focus Control in Optical Tweezer System

The performance of wavefront corrector (WFC) plays a decisive role in the overall effectiveness of an adaptive optical (AO) system. When compared with commonly used WFCs, magnetic fluid deformable mirrors (MFDMs) have advantages of large stroke, low cost, and smooth continuous surface. However, the application of MFDMs is limited by the measuring range of the wavefront sensor (WFS) in the traditional AO systems. In addition, WFS needs one more optical path to measure the wavefront, which makes the AO system structure complex. In this article, a wavefront sensorless AO system based on MFDM is proposed to eliminate the limitations imposed by WFS. The stochastic parallel gradient descent (SPGD) algorithm is introduced in the control system to deform the surface of MFDM without the need to measure the wavefront. The MFDM with the proposed SPGD algorithm is applied to an optical tweezer system to eliminate the aberration caused by the optical system error and refractive index mismatch. Experimental results show that the divergent beam can be adaptively focused to successfully capture micro-particles.

Error analysis and threshold selection in model-based wavefront sensorless adaptive optics system

Accurate values of masked detector signal (MDS) is the key to the correction capability of wavefront sensorless (WFSless) model-based adaptive optics (AO) system. However, imaging detectors always carry different kinds of noises in real applications, which make MDS deviate from theoretical results. Calculation errors of MDS suffered from the noise were analyzed through theory and simulation. Additionally, considering the method of threshold is usually used to mitigate the noise, we also discussed the influence of threshold on MDS. Results showed that the simulation is consistent with the theory and there exists an optimal threshold to make the error minimum for different noise. A fitting formula, which can accurately calculate optimal thresholds, was proposed based on the simulated data. Above results can provide a plausible method and theoretical basis to mitigate imaging noise of detectors in real applications.

Wavefront sensorless adaptive optics system for extended objects based on linear phase diversity technique

Wavefront sensorless (WFSless) adaptive optics (AO) systems have been widely studied in recent years. However, most of the existing WFSless AO systems require point sources for wavefront sensing. Aimed to correct the turbulence-degraded imaging of extended objects, this document proposes a fast wavefront correction approach for a WFSless AO system based on linear phase diversity (PD) technique. By linearizing the system optical transfer function (OTF) and expanding the influence functions of the deformable mirror (DM) on Zernike basis, this method can establish a linear relationship model between the drive voltages of the DM and the CCD acquired extended images. Then the control signal of the DM can be directly output to generate a compensated wavefront from CCD images and fast adaptive wavefront correction can be realized. Numerical simulations and an experimental validation are performed under different turbulence strengths, the results show that the proposed method has an impressive improvement of root mean square (RMS) of wavefront and can effectively restore the turbulence-degraded extended images with a relatively fast correction speed.

Aberration balancing using an image-sharpness metric.

Image-sharpness metrics can be used to optimize optical systems and to control wavefront sensorless adaptive optics systems. We show that for an aberrated system, the numerical value of an image-sharpness metric can be improved by adding specific aberrations. The optimum amplitudes of the additional aberrations depend on the power spectral density of the spatial frequencies of the object.

DNN-based aberration correction in a wavefront sensorless adaptive optics system.

Existing wavefront sensorless (WFS-less) adaptive optics (AO) generally require a search algorithm that takes lots of iterations and measurements to get optimal results. So the latency is a serious problem in the current WFS-less AO system, especially in applications to free-space optics communication. To solve this issue, we propose a deep neural network (DNN)-based aberration correction method. The DNN model can detect the wavefront distortion directly from the intensity images, thereby avoiding time-consuming iterative processes. Since the tip-and-tilt mode of Zernike coefficients are considered, the tip-tilt correction system is not necessarily required in the proposed method. From our simulation results, the proposed method can effectively reduce the computation time and has an impressive improvement of root mean square (RMS) in different turbulence conditions.

Build the Structure of WFSless AO System Through Deep Reinforcement Learning

We report on an aberration correction algorithm for a wavefront sensorless adaptive optics (WFSless AO) system based on deep reinforcement learning. First, it is verified that the reinforcement learning theory can be applied in our system. In addition, the deep deterministic policy gradient algorithm is introduced to build the control structure. After that, deep learning is used to deal with the messy raw images of far-field intensity distribution. We emphatically present how to design a feature extraction with the convolutional neural network in the control structure. To demonstrate the performance of this structure, some comparisons are made with the stochastic parallel gradient descent algorithm and the WFSless AO based on general modes algorithm. The results indicate that the correction speed of our method improves about 9 times and 2.5 times, respectively, for the similar correction effect.

Self-learning control for wavefront sensorless adaptive optics system through deep reinforcement learning

An aberration correction algorithm for wavefront sensorless adaptive optics (WFSless AO) systems based on deep reinforcement learning is presented. An actor–critic structure is designed to evaluate a control policy through the deep deterministic policy gradient (DDPG) algorithm. The algorithm performance is verified with a set-up simulation environment. According to the correction results, the aberration correction process can be expressed as a Markov decision process (MDP). The method exhibits excellent performances in correction capacity and speed. Similar correction effects are obtained with the stochastic parallel-gradient descent (SPGD) algorithm and WFSless AO based on the general-modes (AOG) algorithm. Moreover, the correction speed is improved by approximately 9 and 2.5 times, respectively.

Fast Restoration of Aberration-Degraded Extended Object Based on Local Region Abstraction

The compensating capability of wavefront sensorless adaptive optics (WFSless AO) system for extended object images degraded by aberration is severely limited to correction speed due to largely sized images and low frame rate of camera. To improve the correction capability of WFSless AO system, a method based on local region abstraction (LRA) is presented according to the sparse characteristics of extended object imaging. The experiment results of image restoration with WFSless AO system based on LRA show that the correction speed of this method is 8–20 times faster than that of the previous method for the static aberration, the image restoration speed is improved by about 3.5 times under the dynamic aberration.

Fast correction approach for wavefront sensorless adaptive optics based on a linear phase diversity technique.

Wavefront sensorless (WFSless) adaptive optics (AO) systems have been widely studied in recent years. To reach optimum results, such systems require an efficient correction method. This paper presents a fast wavefront correction approach for a WFSless AO system mainly based on the linear phase diversity (PD) technique. The fast closed-loop control algorithm is set up based on the linear relationship between the drive voltage of the deformable mirror (DM) and the far-field images of the system, which is obtained through the linear PD algorithm combined with the influence function of the DM. A large number of phase screens under different turbulence strengths are simulated to test the performance of the proposed method. The numerical simulation results show that the method has fast convergence rate and strong correction ability, a few correction times can achieve good correction results, and can effectively improve the imaging quality of the system while needing fewer measurements of CCD data.

A high speed model-based approach for wavefront sensorless adaptive optics systems

To improve temporal-frequency property of wavefront sensorless adaptive optics (AO) systems, a fast general model-based aberration correction algorithm is presented. The fast general model-based approach is based on the approximately linear relation between the mean square of the aberration gradients and the second moment of far-field intensity distribution. The presented model-based method is capable of completing a mode aberration effective correction just applying one disturbing onto the deformable mirror(one correction by one disturbing), which is reconstructed by the singular value decomposing the correlation matrix of the Zernike functions’ gradients. Numerical simulations of AO corrections under the various random and dynamic aberrations are implemented. The simulation results indicate that the equivalent control bandwidth is 2–3 times than that of the previous method with one aberration correction after applying N times disturbing onto the deformable mirror (one correction by N disturbing).

Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg–Saxton-based phase retrieval algorithm

Practical orbital angular momentum (OAM)-based free-space optical (FSO) communications commonly experience serious performance degradation and crosstalk due to atmospheric turbulence. In this paper, we propose a wave-front sensorless adaptive optics (WSAO) system with a modified Gerchberg–Saxton (GS)-based phase retrieval algorithm to correct distorted OAM beams. We use the spatial phase perturbation (SPP) GS algorithm with a distorted probe Gaussian beam as the only input. The principle and parameter selections of the algorithm are analyzed, and the performance of the algorithm is discussed. The simulation results show that the proposed adaptive optics (AO) system can significantly compensate for distorted OAM beams in single-channel or multiplexed OAM systems, which provides new insights into adaptive correction systems using OAM beams.

Synchronous model-based approach for wavefront sensorless adaptive optics system.

In this paper, an aberration correction algorithm for wavefront sensorless adaptive optics system is presented, which is based on the approximately linear relation between the mean square of the aberration gradients and the second moment of far-field intensity distribution. To demonstrate algorithm's performance, an experiment system of aberration corrections for Fresnel zone plates imaging is set up. The correction results show the excellent performance of this method in correction speed, and correction capability. Comparing with conventional stochastic parallel gradient descent (SPGD) algorithm, the correction speed with this method is improved about four times for the similar correction effect.

模型式无波前探测自适应光学系统抗噪能力分析

模型式无波前探测自适应光学系统抗噪能力分析