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

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

Fig. 2. Commonly used activation function. (a) ReLU function; (b) sigmoid function; (c) tanh function
![Structure of CNN7 model[34]](/Images/icon/loading.gif)
Fig. 3. Structure of CNN7 model[34]
![Training architecture of weights-sharing two-stream CNN framework[42]](/Images/icon/loading.gif)
Fig. 4. Training architecture of weights-sharing two-stream CNN framework[42]
![Sketch map of feature-based wavefront retrieval approach[47]](/Images/icon/loading.gif)
Fig. 5. Sketch map of feature-based wavefront retrieval approach[47]
![Simulation results of reconstruction images [49]](/Images/icon/loading.gif)
Fig. 6. Simulation results of reconstruction images [49]
![False rates of different methods under low signal-to-noise ratio [52]](/Images/icon/loading.gif)
Fig. 7. False rates of different methods under low signal-to-noise ratio [52]
![ISNet architecture[57]](/Images/icon/loading.gif)
Fig. 8. ISNet architecture[57]
![Architecture of SH-Net[59]. (a) Process of phase retrieval; (b) residual block architecture](/Images/icon/loading.gif)
Fig. 9. Architecture of SH-Net[59]. (a) Process of phase retrieval; (b) residual block architecture

Fig. 10. Diffractive neural network

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