Frontiers in Physics (Jan 2024)
Higher-resolution wavefront sensing based on sub-wavefront information extraction
- Hongli Guan,
- Hongli Guan,
- Hongli Guan,
- Hongli Guan,
- Wang Zhao,
- Wang Zhao,
- Wang Zhao,
- Shuai Wang,
- Shuai Wang,
- Shuai Wang,
- Kangjian Yang,
- Kangjian Yang,
- Kangjian Yang,
- Mengmeng Zhao,
- Mengmeng Zhao,
- Mengmeng Zhao,
- Shenghu Liu,
- Shenghu Liu,
- Shenghu Liu,
- Shenghu Liu,
- Han Guo,
- Han Guo,
- Han Guo,
- Ping Yang,
- Ping Yang,
- Ping Yang
Affiliations
- Hongli Guan
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Hongli Guan
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Hongli Guan
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Hongli Guan
- School of Optoelectronics, University of Chinese Academy of Sciences, Beijing, China
- Wang Zhao
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Wang Zhao
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Wang Zhao
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Shuai Wang
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Shuai Wang
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Shuai Wang
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Kangjian Yang
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Kangjian Yang
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Kangjian Yang
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Mengmeng Zhao
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Mengmeng Zhao
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Mengmeng Zhao
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Shenghu Liu
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Shenghu Liu
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Shenghu Liu
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Shenghu Liu
- School of Optoelectronics, University of Chinese Academy of Sciences, Beijing, China
- Han Guo
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Han Guo
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Han Guo
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Ping Yang
- National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China
- Ping Yang
- Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- Ping Yang
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, China
- DOI
- https://doi.org/10.3389/fphy.2023.1336651
- Journal volume & issue
-
Vol. 11
Abstract
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.
Keywords
- Shack–Hartmann wavefront sensor
- high-resolution wavefront sensing
- sub-wavefront information extraction
- phase modulation
- neural network
