Deep learning wavefront sensing and aberration correction in atmospheric turbulence

Kaiqiang Wang; MengMeng Zhang; Ju Tang; Lingke Wang; Liusen Hu; Xiaoyan Wu; Wei Li; Jianglei Di; Guodong Liu; Jianlin Zhao

doi:10.1186/s43074-021-00030-4

Deep learning wavefront sensing and aberration correction in atmospheric turbulence

doi: 10.1186/s43074-021-00030-4

基金项目:

National Natural Science Foundation of China (61927810, 62075183).

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出版历程
- 收稿日期: 2021-02-19
- 录用日期: 2021-04-20
- 网络出版日期: 2021-06-02

Deep learning wavefront sensing and aberration correction in atmospheric turbulence

1.
MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions, Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, 710129, Xi’an, China
2.
Institute of Fluid Physics, China Academy of Engineering Physics, 621900, Mianyang, China

Funds:

National Natural Science Foundation of China (61927810, 62075183).

摘要

摘要: Deep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor (i.e. reconstruction of the wavefront aberration phase from the distorted image of the object). We compared and found the characteristics of the direct and indirect reconstruction ways:(i) directly reconstructing the aberration phase; (ii) reconstructing the Zernike coefficients and then calculating the aberration phase. We verified the generalization ability and performance of the network for a single object and multiple objects. What's more, we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.
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Abstract: Deep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor (i.e. reconstruction of the wavefront aberration phase from the distorted image of the object). We compared and found the characteristics of the direct and indirect reconstruction ways: (i) directly reconstructing the aberration phase; (ii) reconstructing the Zernike coefficients and then calculating the aberration phase. We verified the generalization ability and performance of the network for a single object and multiple objects. What’s more, we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.
- Wavefront sensing /
- Aberration correction /
- Deep learning

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参考文献(33)

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Deep learning wavefront sensing and aberration correction in atmospheric turbulence

doi: 10.1186/s43074-021-00030-4

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Deep learning wavefront sensing and aberration correction in atmospheric turbulence

doi: 10.1186/s43074-021-00030-4

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Deep learning wavefront sensing and aberration correction in atmospheric turbulence

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出版历程

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