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    Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 16 >Page 1600001 > Article
    • Laser & Optoelectronics Progress
    • Vol. 58, Issue 16, 1600001 (2021)
    Research Progress in the Applications of Convolutional Neural Networks in Optical Information Processing
    Jianglei Di1、2、*, Ju Tang1、2, Ji Wu1、2, Kaiqiang Wang1、2, Zhenbo Ren1、2, Mengmeng Zhang1、2, and Jianlin Zhao1、2、**
    Author Affiliations
  • 1Key Laboratory of Light Field Manipulation and Information Acquisition, Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
  • 2Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
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    DOI: 10.3788/LOP202158.1600001 Cite this Article Set citation alerts
    Jianglei Di, Ju Tang, Ji Wu, Kaiqiang Wang, Zhenbo Ren, Mengmeng Zhang, Jianlin Zhao. Research Progress in the Applications of Convolutional Neural Networks in Optical Information Processing[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1600001 Copy Citation Text show less
    Simulation process. (a) Physical model; (b) forward problems fitting by neural network; (c) inverse problems fitting by neural network
    Fig. 1. Simulation process. (a) Physical model; (b) forward problems fitting by neural network; (c) inverse problems fitting by neural network
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    Simulation process. (a) Fully connected structure; (b) convolution operation
    Fig. 2. Simulation process. (a) Fully connected structure; (b) convolution operation
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    Curve of activation function
    Fig. 3. Curve of activation function
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    Main structure of network. (a) Backbone; (b) FCN; (c) U-net; (d) GAN
    Fig. 4. Main structure of network. (a) Backbone; (b) FCN; (c) U-net; (d) GAN
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    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. 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
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    Flow chart of network training and testing. (a) Training process; (b) testing process
    Fig. 6. Flow chart of network training and testing. (a) Training process; (b) testing process
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    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. 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]
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    Off-axis holographic numerical reconstruction with CNN. (a) U-net [34]; (b) Y-Net [36]
    Fig. 8. Off-axis holographic numerical reconstruction with CNN. (a) U-net [34]; (b) Y-Net [36]
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    Applications of CNN in holographic reconstruction distance. (a) Regression model[42-44]; (b) classification model[47]
    Fig. 9. Applications of CNN in holographic reconstruction distance. (a) Regression model[42-44]; (b) classification model[47]
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    Fringe patterns analysis with CNN. (a) Method in Ref. [58]; (b) method in Ref. [59]; (c) method in Ref. [60]
    Fig. 10. Fringe patterns analysis with CNN. (a) Method in Ref. [58]; (b) method in Ref. [59]; (c) method in Ref. [60]
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    Phase unwrapping with CNN. (a) Method in Ref. [79]; (b) method in Ref. [80]; (c) method in Ref. [84]
    Fig. 11. Phase unwrapping with CNN. (a) Method in Ref. [79]; (b) method in Ref. [80]; (c) method in Ref. [84]
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    Ghost imaging technology. (a) Computational ghost imaging process; (b) ghost imaging reconstruction using neural network
    Fig. 12. Ghost imaging technology. (a) Computational ghost imaging process; (b) ghost imaging reconstruction using neural network
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    Computational ghost imaging with CNN. (a) DRU-Net [99]; (b) DeepGhost [101]; (c) DAttNet [102]
    Fig. 13. Computational ghost imaging with CNN. (a) DRU-Net [99]; (b) DeepGhost [101]; (c) DAttNet [102]
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    Fourier ptychographic microscopy system[116]
    Fig. 14. Fourier ptychographic microscopy system[116]
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    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. 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]
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    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. 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]
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    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. 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]
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    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. 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]
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    Accurate choroidal segmentation using CNN[157]
    Fig. 19. Accurate choroidal segmentation using CNN[157]
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    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]
    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]
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    Application fieldNetwork structureLoss functionApplication problems
    Digital holographyBackbone, U-net, GANMSE, MAE, cross entropyHolographic reconstruction[30-39]: “twin-image” problem, “end to end” phase recovery, reconstruction of complex amplitude light fieldAuto focusing[42-49]: prediction of holographic reconstruction distanceOthers[50-54]: holographic image denoising, multi wavelength hologram fusion and reconstruction, reconstructed image enhancement
    Fringe analysisBackbone, FCN, U-netMSE, MAE, regularizationPhase demodulation and 3D reconstruction[58-62,67]Fast recognition of fringes[63]Fringe image denoising[64-66]
    Phase unwrappingFCN, U-net, GANMSE, MAE, cross entropy, regularizationPhase unwrapping[79-86]
    Application fieldNetwork structureLoss functionApplication problems
    Ghost imagingBackbone, FCN, U-net, GANMSE, regularizationNoise suppression[94]Blind image reconstruction[95,97]Low sampling imaging[96,101-103,105]Lighting mode optimization[98,102-104]
    Fourier ptychographic microscopyU-net, GANMSE, MAE, regularizationSuper resolution image reconstruction [112-113,115-116]Speed up reconstruction [113,115]Position deviation correction [117]Noise suppression[113-115]
    Super resolution imagingFCN, U-net, GANMSE, MAE, regularizationSuper resolution imaging [120-131]
    Scattering medium imagingBackbone, U-netMSE, MAE, cross entropyTarget classification [142-145]Image reconstruction [146-153]Modal decomposition of multimode fiber [154]
    Optical tomographyBackbone, U-net, GANMSE, cross entropy, regularizationCoherence tomography [156-160]: high precision and fast image segmentation, image enhancementDiffraction tomography[164-167]: noise suppression, Inversion reconstruction
    Table 1. Applications of CNN in optical information processing
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    Jianglei Di, Ju Tang, Ji Wu, Kaiqiang Wang, Zhenbo Ren, Mengmeng Zhang, Jianlin Zhao. Research Progress in the Applications of Convolutional Neural Networks in Optical Information Processing[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1600001
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