Aberrations degrade the accuracy of quantitative, imaging-based measurements, like particle image velocimetry (PIV). Adaptive optical elements can in principle correct the wavefront distortions, but are limited by their technical specifications. Here we propose an actuator-free correction based on a multiple-input deep convolutional neural network which uses an additional input from a wavefront sensor to correct time-varying distortions. It is applied for imaging flow velocimetry to conduct measurements through a fluctuating air-water phase boundary. Dataset for neural network is generated by an experimental setup with a deformable mirror. Correction performance of trained model is estimated in terms of image quality, which is improved significantly, and flow measurement results, where the errors induced by the distortion from fluctuating phase boundary can be corrected by 82 %. The technique has the potential to replace classical closed-loop adaptive optical systems where the performance of the actuators is not sufficient.

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.

Adaptive Optics (AO) technology is an effective means to compensate for wavefront distortion, but its inherent delay error will cause the compensation wavefront on the deformable mirror (DM) to lag behind the changes in the distorted wavefront. Especially when the change in the wavefront is higher than the Shack–Hartmann wavefront sensor (SHWS) sampling frequency, the multi-frame delay will seriously limit its correction performance. In this paper, a highly stable AO prediction network based on deep learning is proposed, which only uses 10 frames of prior wavefront information to obtain high-stability and high-precision open-loop predicted slopes for the next six frames. The simulation results under various distortion intensities show that the prediction accuracy of six frames decreases by no more than 15%, and the experimental results also verify that the open-loop correction accuracy of our proposed method under the sampling frequency of 500 Hz is better than that of the traditional non-predicted method under 1000 Hz.

Multi-line-of-sight wavefront sensing, crucial for next-generation astronomy and laser applications, often increases system complexity by adding sensors. This research introduces, to the best of our knowledge, a novel method for multi-line-of-sight Hartmann-Shack wavefront sensing by using a single sensor, addressing challenges in centroid estimation and classification under atmospheric turbulence. This method contrasts with existing techniques that rely on multiple sensors, thereby reducing system complexity. Innovations include combining edge detection and peak extraction for precise centroid calculation, improved k-means clustering for robust centroid classification, and a centroid filling algorithm for subapertures with light loss. The method’s effectiveness was confirmed through simulations for a five-line-of-sight system and experimental setup for two-line and three-line-of-sight systems, demonstrating its potential in real atmospheric aberration correction conditions. Experimental findings indicate that, when implemented in a closed-loop configuration, the method significantly reduces wavefront residuals from 1 λ to 0.1 λ under authentic atmospheric turbulence conditions. Correspondingly, the quality of the far-field spot is enhanced by a factor of 2 to 4. These outcomes collectively highlight the method’s robust capability in enhancing optical system performance in environments characterized by genuine atmospheric turbulence.