• Acta Photonica Sinica
  • Vol. 49, Issue 6, 0610002 (2020)
Wei FENG1、2, Xiao-dong ZHAO1, Gui-ming WU1, Zhong-hui YE1, and Da-xing ZHAO1、*
Author Affiliations
  • 1School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China
  • 2Hubei Key Laboratory of Modern Manufacturing Quality Engineering, Wuhan 430068, China
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    DOI: 10.3788/gzxb20204906.0610002 Cite this Article
    Wei FENG, Xiao-dong ZHAO, Gui-ming WU, Zhong-hui YE, Da-xing ZHAO. Computational Ghost Imaging Method Based on Convolutional Neural Network[J]. Acta Photonica Sinica, 2020, 49(6): 0610002 Copy Citation Text show less
    Principle schematic
    Fig. 1. Principle schematic
    Flow chart of algorithm
    Fig. 2. Flow chart of algorithm
    Convolutional neural network model
    Fig. 3. Convolutional neural network model
    Comparison of numerical simulation results of different methods
    Fig. 4. Comparison of numerical simulation results of different methods
    Experimental setup
    Fig. 5. Experimental setup
    Simulated speckle and actual speckle
    Fig. 6. Simulated speckle and actual speckle
    Comparison of experimental actual effects of different methods
    Fig. 7. Comparison of experimental actual effects of different methods
    PSNR and SSIM at different sampling times
    Fig. 8. PSNR and SSIM at different sampling times
    CSCGICNN-CGI
    β=0.083.159 00.063 3
    β=0.169.728 70.058 9
    Table 1. Average running time of each algorithm for a single test image at different sampling times (unit: s)
    Wei FENG, Xiao-dong ZHAO, Gui-ming WU, Zhong-hui YE, Da-xing ZHAO. Computational Ghost Imaging Method Based on Convolutional Neural Network[J]. Acta Photonica Sinica, 2020, 49(6): 0610002
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