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Deep learning wavefront sensing for fine phasing of segmented mirrors
Y. Wang; F. Jiang; G. Ju; B. Xu; Q. An; C. Zhang; S. Wang and S. Xu
2021
发表期刊Optics Express
ISSN10944087
卷号29期号:16页码:25960-25978
摘要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. 2021 Optical Society of America.
DOI10.1364/OE.434024
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收录类别SCI ; EI
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/65129
专题中国科学院长春光学精密机械与物理研究所
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Y. Wang,F. Jiang,G. Ju,et al. Deep learning wavefront sensing for fine phasing of segmented mirrors[J]. Optics Express,2021,29(16):25960-25978.
APA Y. Wang.,F. Jiang.,G. Ju.,B. Xu.,Q. An.,...&S. Wang and S. Xu.(2021).Deep learning wavefront sensing for fine phasing of segmented mirrors.Optics Express,29(16),25960-25978.
MLA Y. Wang,et al."Deep learning wavefront sensing for fine phasing of segmented mirrors".Optics Express 29.16(2021):25960-25978.
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