Search Results (13)

In wavefront sensorless adaptive optics (WFS-less AO) systems, stochastic parallel gradient descent (SPGD) is the primary optimization method for correcting wavefront distortions. However, as the intensity of atmospheric turbulence interference increases, the fixed gain coefficient of the SPGD algorithm results in significant decreases in convergence speed and precision. Moreover, the algorithm is inclined to local optima, thus failing to satisfy the requirements for real-time wavefront distortion correction. To address these issues, this paper introduces a new optimization algorithm, Sophia optimized stochastic parallel gradient descent (Sophia-SPGD), which is based on second-order clipped stochastic optimization in deep learning. This algorithm computes the first-order and second-order moments of the performance metrics from its first and second gradients, respectively, and dynamically modulates the gain via a shearing mechanism to increase the convergence speed and diminish the probability of falling into local optima. Numerical simulations and experiments demonstrate that under strong turbulence conditions, the performance of Sophia-SPGD surpasses that of the traditional SPGD algorithm. Full article

Free Space Optical (FSO) communication is extensively utilized in the telecommunication industry for both ground and space wireless links, as well as last-mile applications, as a result of its lesser Bit Error Rate (BER), free spectrum, and easy relocation. However, atmospheric turbulence, also known as Wavefront Aberration (WA), is considered a serious issue because it causes higher BER and affects coupling efficiency. In order to address this issue, a Sensor-Less Adaptive Optics (SLAO) system is developed for FSO to enhance performance. In this research, the compensation of WA in SLAO is obtained by proposing the Brownian motion and Directional mutation scheme-based Coati Optimization Algorithm, BDCOA. Here, the BDCOA is developed to search for an optimum control signal value of actuators in Deformable Mirror (DM). The incorporated Brownian motion and directional mutation are used to avoid the local optimum issue and enhance search space efficiency while searching for the control signal. Therefore, the dynamic control signal optimization for DM using BDCOA helps to enhance the coupling efficiency. Thus, the WAs are compensated for and optical signal concentration is enhanced in FSO. The metrics used for analyzing the BDCOA are Root Mean Square (RMS), BER, coupling efficiency, and Strehl Ratio (SR). The existing methods, such as Simulated Annealing (SA) and Stochastic Parallel Gradient Descent (SPGD), Advanced Multi-Feedback SPGD (AMFSPGD), and Oppositional-Breeding Artificial Fish Swarm (OBAFS), are used for evaluating the performance of BDCOA. The RMS of BDCOA for iterations 500 is 0.12, which is less than that of the SA-SPGD and OBAFS. Full article

The correction of wavefront aberrations in wavefront sensorless (WFS-less) adaptive optical (AO) systems requires control algorithms that can ensure rapid convergence while maintaining effective correction capabilities. This paper proposes a novel control algorithm based on the RUNge Kutta optimizer (RUN) for WFS-less AO systems that enables the quick and efficient correction of small aberrations, as well as larger aberrations. To evaluate the convergence speed and correction capabilities of a WFS-less AO system based on the RUN control algorithm, we constructed a simulated AO system and an experimental setup with a 97-element deformable mirror (DM), respectively. Additionally, the results obtained with the Particle Swarm Optimization (PSO) algorithm, Differential Evolution Algorithm (DEA), and Genetic Algorithm (GA) are also provided for comparison and analysis. Both the simulated and experimental results consistently demonstrated that our proposed method outperformed several competing algorithms in terms of correction performance and convergence speed. Furthermore, the experimental results further validate the effectiveness of our control algorithm in scenarios involving significant aberrations. Full article

Optical satellite communications (OSC) downlinks can support much higher bandwidths than radio-frequency channels. However, atmospheric turbulence degrades the optical beam wavefront, leading to reduced data transfer rates. In this study, we propose using reinforcement learning (RL) as a lower-cost alternative to standard wavefront sensor-based solutions. We estimate that RL has the potential to reduce system latency, while lowering system costs by omitting the wavefront sensor and low-latency wavefront processing electronics. This is achieved by adopting a control policy learned through interactions with a cost-effective and ultra-fast readout of a low-dimensional photodetector array, rather than relying on a wavefront phase profiling camera. However, RL-based wavefront sensorless adaptive optics (AO) for OSC downlinks faces challenges relating to prediction latency, sample efficiency, and adaptability. To gain a deeper insight into these challenges, we have developed and shared the first OSC downlink RL environment and evaluated a diverse set of deep RL algorithms in the environment. Our results indicate that the Proximal Policy Optimization (PPO) algorithm outperforms the Soft Actor–Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Moreover, PPO converges to within $86\%$ of the maximum performance achievable by the predominant Shack–Hartmann wavefront sensor-based AO system. Our findings indicate the potential of RL in replacing wavefront sensor-based AO while reducing the cost of OSC downlinks. Full article

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. Full article

Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms’ convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm. Full article

Optical microscopy is a vital tool for visualizing the cellular and sub-cellular structures of biological specimens. However, due to its limited penetration depth, its biological applicability has been hindered. The scattering and absorption of light by a wide array of biomolecules causes signal attenuation and restricted imaging depth in tissues. Researchers have put forth various approaches to address this, including designing novel probes for imaging applications and introducing adaptive optics (AO) technology. Various techniques, such as direct wavefront sensing to quickly detect and fix wavefront deformation and indirect wavefront sensing using modal and zonal methods to rectify complex aberrations, have been developed through AO paradigms. In addition, algorithmic post-processing without mechanical feedback has been utilized to correct the optical patterns using the matrix-based method. Hence, reliable optical imaging through thick biological tissue is made possible by sensorless AO. This review highlights the latest advancements in various AO-based optical microscopy techniques for depth-resolved imaging and briefly discusses their potential in various biomedical applications. Full article

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. Full article

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. Full article

In space optical communication, owing to the influence of atmospheric turbulence, optical beams lose focus and become phase-distorted, which reduces the communication quality. Considering the polarization dependence of liquid crystal spatial light modulators and the dispersion effect of liquid crystal materials, the energy utilization rate of liquid crystal adaptive optics systems is low. In this study, a dual liquid crystal spatial light modulator adaptive optics system based on the GS algorithm is used to correct the wavefront distortion of a signal beam under different atmospheric turbulence intensities, and the Strehl ratio (SR) is used as the evaluation index. The simulation results show that the SR of the corrected system can be increased from 0.23, 0.41, and 0.72 to 0.77, 0.89, and 0.95, respectively. The corrected beam spot was more concentrated and the light intensity at the center of the beam spot was stronger. The experimental results show that, after the distortion wavefront is corrected by the dual liquid crystal spatial light modulator, the average gray value of the 10 × 10 pixels in the center of the spot increases from 159.3, 113.1, and 58.4 to 253.4, 247.7, and 198.3, respectively. Full article

Sensor-less adaptive optics (SLAO) based on stochastic parallel gradient descent (SPGD) is effective for the compensation of atmospheric turbulence in coherent free-space optical communication (CFSOC) systems. However, SPGD converges slowly and easily falls into local extremes. Therefore, we propose a novel NadamSPGD algorithm for efficient wavefront correction that combines Nesterov-accelerated adaptive moment estimation (Nadam) and SPGD. Specifically, Nesterov’s accelerated gradient momentum (NAG) and adaptive gain coefficients are integrated to conventional SPGD to accelerate its convergence speed and avoid converging to extremum points. Theoretical analysis, numerical simulations and experimental results demonstrate that NadamSPGD can increase the convergence speed by ~50% and significantly improve the robustness of parameters, and thus more efficiently suppress the negative effects of atmospheric turbulence on mixing efficiency (ME) and bit error rate (BER). Our algorithm also presents better dynamic performance under strong turbulence and high Greenwood frequency conditions, and it is more suitable for real-time SLAO systems. This study proves that the NadamSPGD algorithm is suitable for SLAO in the CFSOC system and is a viable substitute for SPGD to improve the quality of optical communications. Full article

Magnetic fluid is a stable colloidal suspension of nano-sized, single-domain ferri/ferromagnetic particles dispersed in a liquid carrier. The liquid can be magnetized by the ferromagnetic particles aligned with the external magnetic field, which can be used as a wavefront corrector to correct the large aberrations up to more than 100 µm in adaptive optics (AO) systems. Since the measuring range of the wavefront sensor is normally small, the application of the magnetic fluid deformable mirror (MFDM) is limited with the WFS based AO system. In this paper, based on the MFDM model and the relationship between the second moment (SM) of the aberration gradients and the far-field intensity distribution, a model-based wavefront sensorless (WFSless) control algorithm is proposed for the MFDM. The correction performance of MFDM using the model-based control algorithm is evaluated in a WFSless AO system setup with a prototype MFDM, where a laser beam with unknown aberrations is supposed to produce a focused spot on the CCD. Experimental results show that the MFDM can be used to effectively compensate for unknown aberrations in the imaging system with the proposed model-based control algorithm. Full article

For high-speed aircraft, a conformal window is used to optimize the aerodynamic performance. However, the local shape of the conformal window leads to large amounts of dynamic aberrations varying with look angle. In this paper, deformable mirror (DM) and model-based wavefront sensorless adaptive optics (WSLAO) are used for dynamic aberration correction of an infrared remote sensor equipped with a conformal window and scanning mirror. In model-based WSLAO, aberration is captured using Lukosz mode, and we use the low spatial frequency content of the image spectral density as the metric function. Simulations show that aberrations induced by the conformal window are dominated by some low-order Lukosz modes. To optimize the dynamic correction, we can only correct dominant Lukosz modes and the image size can be minimized to reduce the time required to compute the metric function. In our experiment, a 37-channel DM is used to mimic the dynamic aberration of conformal window with scanning rate of 10 degrees per second. A 52-channel DM is used for correction. For a 128 × 128 image, the mean value of image sharpness during dynamic correction is 1.436 × 10⁻⁵ in optimized correction and is 1.427 × 10⁻⁵ in un-optimized correction. We also demonstrated that model-based WSLAO can achieve convergence two times faster than traditional stochastic parallel gradient descent (SPGD) method. Full article