Correction performance of an adaptive optics (AO) system is severely limited by its system latency under high temporal frequency distortions. Wavefront prediction methods has been proven to be an effective way to compensate system delay. A novel wavefront prediction method based on residual learning fusion network (RLFNet) is proposed in this paper. The network is able to eliminate redundant information between adjacent wavefront frames and fuse refined features, which provides a higher prediction accuracy under fewer priori wavefront input. The method is tested on a 1km laser atmospheric transmission experimental setup through high frequency atmospheric turbulence, where wavefront data is collected from a 1kHz Shack-Hartmann wavefront sensor (SHWS). We show that the proposed architecture is able to predict the second frame wavefront after the previous 6 frames with a root-mean-square (RMS) wavefront error reduction up to 53% compare to non-predictive method.

This Letter introduces the idea of unsupervised learning into object-independent wavefront sensing for the first time, to the best of our knowledge, which can achieve fast phase recovery of arbitrary objects without labels. First, a fine feature extraction method which only depends on the wavefront aberrations is proposed. Then, a lightweight neural network and an optical feature system are combined to form an unsupervised learning model, and the neural network is promoted to be well trained by reversely outputting fine features. Simulation results prove that the proposed method can effectively overcome the aberrations (static or variable) existing in the optical system and achieve wavefront sensing of different objects with high precision and efficiency.

Adaptive optics (AO) technology is an effective means to compensate for atmospheric turbulence, but the inherent delay error of an AO system will cause the compensation phase of the deformable mirror (DM) to lag behind the actual distortion, which limits the correction performance of the AO technology. Therefore, the feed-forward prediction of atmospheric turbulence has important research value and application significance to offset the inherent time delay and improve the correction bandwidth of the AO system. However, most prediction algorithms are limited to an open-loop system, and the deployment and the application in the actual AO system are rarely reported, so its correction performance improvement has not been verified in practice. We report, to our knowledge, the first successful test of a deep learning-based spatiotemporal prediction model in an actual 3 km laser atmospheric transport AO system and compare it with the traditional closed-loop control methods, demonstrating that the AO system with the prediction model has higher correction performance.

Light field salient object detection plays an important role in computer vision tasks because of the excellent performance on challenging scenes. However, the imprecise extraction of saliency features from light field imaging puts a negative impact on the accuracy of saliency results, and the invalid fusion of these extracted features makes saliency results lose details, further cutting down their accuracy. In this article, to effectively extract and fuse saliency features of light field imaging, we propose an attention-oriented refinement and fusion network to solve these problems, which mainly includes the attention-oriented refinement module (ARM) and the attention-oriented fusion module (AFM). Specifically, ARM can precisely refine similar features between focal slices and all-focus image, and fully extract abundant structural characteristics of the light field. Subsequently, AFM can efficiently fuse those features extracted by ARM to obtain saliency results with detail information. Furthermore, different dilated convolution layers embedded with ARM and AFM are leveraged to further capture complete saliency results with detail information. Experimental results demonstrate that our proposed method outperforms other 20 state-of-the-art methods and achieves Top-1 accuracies on three light field datasets.

A real-time wavefront sensing method for arbitrary targets is proposed, which provides an effective way for diversified wavefront sensing application scenarios. By using a distorted grating, the positive and negative defocus images are simultaneously acquired on a single detector. A fine feature, which is independent of the target itself but corresponding to the wavefront aberration, is defined. A lightweight and efficient network combined with an attention mechanism (AM-EffNet) is proposed to establish an accurate mapping between the features and the incident wavefronts. Comparison results show that the proposed method has superior performance compared to other methods and can achieve high-accuracy wavefront sensing in varied target scenes only by using the point target dataset to train the network well.

The reaction force of a large-aperture piezoelectric fast steering mirror (PFSM) has adverse coupling interference for the stability and pointing accuracy of laser beams, and the dynamic characteristics of the reaction force are coupled with the inner components of the PFSM. In order to compensate for and eliminate the reaction force, it is essential to accurately analyze the dynamic characteristics. In this paper, a simplified piezoelectric-coupling model of PFSM is established. The coupling mathematical equations for investigating the characteristics of the reaction force are deducted based on the piezoelectric constitutive equation and Hamiltonian’s principle. Then the coupling characteristics of the reaction force are probed by a finite element (FE) piezoelectric-coupling method. The simulations for three large apertures’ (250, 320, and 400 mm) FE models show that the reaction force has a linear positive correlation with the actuating voltage, and coupled with the materials of the central flexure hinge, the relationship between the reaction force and driving frequency is not completely quadratic. Experiments with the 320 mm aperture are completed, and the testing results are consistent with the mathematical model and the FE piezoelectric-coupling simulation. The dynamic characteristics of the reaction force demonstrated in this paper are significance for the accurate estimation of the reaction force, the design of compensation structure, and the optimization of algorithm for beam jitter controlling.