波前
稳健性(进化)
波前传感器
光学
自适应光学
残余物
计算机科学
非线性系统
变形镜
均方根
相(物质)
算法
物理
化学
基因
量子力学
生物化学
作者
Hao Chen,Ling Wei,Yi He,Jinsheng Yang,Xiqi Li,Lingxiao Li,Linghai Huang,Kai Wei
出处
期刊:Optics Express
[The Optical Society]
日期:2023-01-11
卷期号:31 (2): 2989-2989
被引量:5
摘要
Traditional plenoptic wavefront sensors (PWFS) suffer from the obvious step change of the slope response, leading to poor wavefront detection performance. In order to solve this problem, in this paper, a deep learning model is proposed to restore phase maps directly from slope measurements of PWFS. Numerical simulations are employed to demonstrate our approach, and the statistical residual wavefront root mean square error (RMSE) of our method is 0.0810 ± 0.0258λ, which is much superior to those of modal algorithm (0.2511 ± 0.0587λ) and zonal approach (0.3584 ± 0.0487λ). The internal driving force of PWFS-ResUnet is investigated, and the slope response differences between sub-apertures and directions are considered as a probably key role to help our model to accurately restore the phase map. Additionally, the robustness of our model to turbulence strength and signal-to-noise ratio (SNR) level is also tested. The proposed method provides a new direction to solve the nonlinear problem of traditional PWFS.
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