波前
泽尼克多项式
波前传感器
自适应光学
计算机科学
失真(音乐)
采样(信号处理)
人工智能
人工神经网络
光学
计算机视觉
算法
物理
放大器
计算机网络
带宽(计算)
滤波器(信号处理)
作者
Hongli Guan,Wang Zhao,Shuai Wang,Kangjian Yang,Mingbo Zhao,Shenghu Liu,Han Guo,Ping Yang
标识
DOI:10.3389/fphy.2023.1336651
摘要
The limited spatial sampling rates of conventional Shack–Hartmann wavefront sensors (SHWFSs) make them unable to sense higher-order wavefront distortion. In this study, by etching a known phase on each microlens to modulate sub-wavefront, we propose a higher-resolution wavefront reconstruction method that employs a modified modal Zernike wavefront reconstruction algorithm, in which the reconstruction matrix contains quadratic information that is extracted using a neural network. We validate this method through simulations, and the results show that once the network has been trained, for various atmospheric conditions and spatial sampling rates, the proposed method enables fast and accurate high-resolution wavefront reconstruction. Furthermore, it has highly competitive advantages such as fast dataset generation, simple network structure, and short prediction time.
科研通智能强力驱动
Strongly Powered by AbleSci AI