Open AccessDeep learning wavefront sensing for fine phasing of segmented mirrors
Author(s) -
Yirui Wang,
Fengyi Jiang,
Guohao Ju,
Boqian Xu,
Qichang An,
Chunyue Zhang,
Shuaihui Wang,
Shuyan Xu
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.434024
Subject(s) - wavefront , piston (optics) , computer science , optics , phaser , primary mirror , phase retrieval , artificial neural network , deep learning , tilt (camera) , artificial intelligence , deformable mirror , phase (matter) , active optics , adaptive optics , telescope , physics , mathematics , fourier transform , quantum mechanics , geometry
Segmented primary mirror provides many crucial important advantages for the construction of extra-large space telescopes. The imaging quality of this class of telescope is susceptible to phasing error between primary mirror segments. Deep learning has been widely applied in the field of optical imaging and wavefront sensing, including phasing segmented mirrors. Compared to other image-based phasing techniques, such as phase retrieval and phase diversity, deep learning has the advantage of high efficiency and free of stagnation problem. However, at present deep learning methods are mainly applied to coarse phasing and used to estimate piston error between segments. In this paper, deep Bi-GRU neural work is introduced to fine phasing of segmented mirrors, which not only has a much simpler structure than CNN or LSTM network, but also can effectively solve the gradient vanishing problem in training due to long term dependencies. By incorporating phasing errors (piston and tip-tilt errors), some low-order aberrations as well as other practical considerations, Bi-GRU neural work can effectively be used for fine phasing of segmented mirrors. Simulations and real experiments are used to demonstrate the accuracy and effectiveness of the proposed methods.