Fig. 1. Simulation process. (a) Physical model; (b) forward problems fitting by neural network; (c) inverse problems fitting by neural network

Fig. 2. Simulation process. (a) Fully connected structure; (b) convolution operation

Fig. 3. Curve of activation function

Fig. 4. Main structure of network. (a) Backbone; (b) FCN; (c) U-net; (d) GAN

Fig. 5. Detail structure of network. (a) Residual block; (b) multi-scale block; (c) attention block, in which (c1) is channel attention and (c2) is spatial attention; (d) dense connected block

Fig. 6. Flow chart of network training and testing. (a) Training process; (b) testing process

Fig. 7. In-line holographic numerical reconstruction with CNN. (a) CNN is used to suppress “twin image” and autocorrelation artifacts^[32]; (b) end-to-end phase reconstruction using eHoloNet ^[33]

Fig. 8. Off-axis holographic numerical reconstruction with CNN. (a) U-net ^[34]; (b) Y-Net ^[36]

Fig. 9. Applications of CNN in holographic reconstruction distance. (a) Regression model^[42-44]; (b) classification model^[47]

Fig. 10. Fringe patterns analysis with CNN. (a) Method in Ref. [58]; (b) method in Ref. [59]; (c) method in Ref. [60]

Fig. 11. Phase unwrapping with CNN. (a) Method in Ref. [79]; (b) method in Ref. [80]; (c) method in Ref. [84]

Fig. 12. Ghost imaging technology. (a) Computational ghost imaging process; (b) ghost imaging reconstruction using neural network

Fig. 13. Computational ghost imaging with CNN. (a) DRU-Net ^[99]; (b) DeepGhost ^[101]; (c) DAttNet ^[102]

Fig. 14. Fourier ptychographic microscopy system^[116]

Fig. 15. Applications of CNN in Fourier ptychographic microscopy. (a) Super-resolution reconstruction of complex amplitude lightfield^[115]; (b) aberration-free high resolution image reconstruction with pupil function estimation^[116]; (c) LED array position deviation correction to optimize reconstruction quality^[117]

Fig. 16. Applications of CNN in super-resolution imaging. (a)Cross modal and super-resolution imaging with GAN^[124]; (b) end-to-end lensless microscopic super-resolution imaging ^[127]; (c) hologram super-resolution optimization^[128]

Fig. 17. Applications of CNN in scattering medium classification. (a) Network trained by synthetic data achieves classification in experimental application^[143]; (b) speckle pattern classification of face and non-face^[144]

Fig. 18. Applications of CNN in scattering medium reconstruction. (a) Image reconstruction of speckle field behind optical fiber with CNN^[147]; (b) CNN is used to pre-reconstruct the phase^[148]; (c) CNN for image reconstruction in strong scattering media^[150]

Fig. 19. Accurate choroidal segmentation using CNN^[157]

Fig. 20. Optical fiber reconstruction in optical diffraction tomography using CNN. (a) Internal structure reconstruction of optical fiber with limited angle^[166]; (b) internal structure reconstruction of photonic crystal fiber with sparse angle^[167]