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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

Kaiqiang Wang, MengMeng Zhang, Ju Tang, Lingke Wang, Liusen Hu, Xiaoyan Wu, Wei Li, Jianglei Di, Guodong Liu, Jianlin Zhao. Deep learning wavefront sensing and aberration correction in atmospheric turbulence[J]. PhotoniX. doi: 10.1186/s43074-021-00030-4
引用本文: Kaiqiang Wang, MengMeng Zhang, Ju Tang, Lingke Wang, Liusen Hu, Xiaoyan Wu, Wei Li, Jianglei Di, Guodong Liu, Jianlin Zhao. Deep learning wavefront sensing and aberration correction in atmospheric turbulence[J]. PhotoniX. doi: 10.1186/s43074-021-00030-4
Kaiqiang Wang, MengMeng Zhang, Ju Tang, Lingke Wang, Liusen Hu, Xiaoyan Wu, Wei Li, Jianglei Di, Guodong Liu, Jianlin Zhao. Deep learning wavefront sensing and aberration correction in atmospheric turbulence[J]. PhotoniX. doi: 10.1186/s43074-021-00030-4
Citation: Kaiqiang Wang, MengMeng Zhang, Ju Tang, Lingke Wang, Liusen Hu, Xiaoyan Wu, Wei Li, Jianglei Di, Guodong Liu, Jianlin Zhao. Deep learning wavefront sensing and aberration correction in atmospheric turbulence[J]. PhotoniX. 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).

Deep learning wavefront sensing and aberration correction in atmospheric turbulence

Funds: 

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

  • 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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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

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