Wang Ning, Zhu Licheng, Yuan Qiang, Ge Xinlan, Gao Zeyu, Wang Shuai, Yang Ping
National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China.
Key Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
Sensors (Basel). 2023 Nov 18;23(22):9260. doi: 10.3390/s23229260.
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.
自适应光学（AO）技术是补偿波前畸变的有效手段，但其固有的延迟误差会导致变形镜（DM）上的补偿波前滞后于畸变波前的变化。特别是当波前变化高于夏克-哈特曼波前传感器（SHWS）的采样频率时，多帧延迟将严重限制其校正性能。本文提出了一种基于深度学习的高稳定性AO预测网络，该网络仅使用10帧先前的波前信息即可获得未来六帧的高稳定性和高精度开环预测斜率。各种畸变强度下的仿真结果表明，六帧的预测精度下降不超过15%，实验结果也验证了本文所提方法在500Hz采样频率下的开环校正精度优于传统非预测方法在1000Hz下的精度。
