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Chinese Journal of Lasers, Volume. 50, Issue 11, 1101009(2023)

Research Progress and Prospect of Adaptive Optics Based on Deep Learning

Yiwen Hu1, Xin Liu1, Cuifang Kuang1,2, Xu Liu1,2, and Xiang Hao1、*
Author Affiliations
  • 1College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China
  • 2Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou 311100, Zhejiang, China
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    AbstractGet PDF(in Chinese)
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    References (76)
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    Figures & Tables(10)
    Commonly used artificial neural network models. (a) Fully connected neural network; (b) convolutional neural network; (c) residual network; (d) U-Net architecture; (e) Inception architecture; (f) long short-term memory network

    Fig. 1. Commonly used artificial neural network models. (a) Fully connected neural network; (b) convolutional neural network; (c) residual network; (d) U-Net architecture; (e) Inception architecture; (f) long short-term memory network

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    Commonly used activation function. (a) ReLU function; (b) sigmoid function; (c) tanh function

    Fig. 2. Commonly used activation function. (a) ReLU function; (b) sigmoid function; (c) tanh function

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    Structure of CNN7 model[34]

    Fig. 3. Structure of CNN7 model[34]

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    Training architecture of weights-sharing two-stream CNN framework[42]

    Fig. 4. Training architecture of weights-sharing two-stream CNN framework[42]

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    Sketch map of feature-based wavefront retrieval approach[47]

    Fig. 5. Sketch map of feature-based wavefront retrieval approach[47]

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    Simulation results of reconstruction images [49]

    Fig. 6. Simulation results of reconstruction images [49]

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    False rates of different methods under low signal-to-noise ratio [52]

    Fig. 7. False rates of different methods under low signal-to-noise ratio [52]

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    ISNet architecture[57]

    Fig. 8. ISNet architecture[57]

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    Architecture of SH-Net[59]. (a) Process of phase retrieval; (b) residual block architecture

    Fig. 9. Architecture of SH-Net[59]. (a) Process of phase retrieval; (b) residual block architecture

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    Diffractive neural network

    Fig. 10. Diffractive neural network

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    Yiwen Hu, Xin Liu, Cuifang Kuang, Xu Liu, Xiang Hao. Research Progress and Prospect of Adaptive Optics Based on Deep Learning[J]. Chinese Journal of Lasers, 2023, 50(11): 1101009

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    Paper Information

    Category: laser devices and laser physics

    Received: Jan. 31, 2023

    Accepted: May. 10, 2023

    Published Online: Jun. 5, 2023

    The Author Email: Hao Xiang (haox@zju.edu.cn)

    DOI:10.3788/CJL230470

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology