Optics and Precision Engineering, Volume. 31, Issue 14, 2135(2023)

Image reconstruction based on deep compressive sensing combined with global and local features

Yuanhong ZHONG1,*... Qianfeng XU1, Yujie ZHOU1 and Shanshan WANG2 |Show fewer author(s)
Author Affiliations
  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing400044, China
  • 2Institute of Physical Science and Information Technology, Anhui University, Hefei30039, China
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    Figures & Tables(11)
    Global-to-Local Compressive Sensing Image Reconstruction Model Structure
    8 test images from Set11[32]
    Sampling rate is 10%, and the reconstruction images of each algorithm on the image House are compared
    Sampling rate is 20%, and the reconstruction image comparison of each algorithm on the image Monarch
    Change curve of loss with the number of training iterations (epochs) at 20% sampling rate
    G2LNet reconstruction image and filter flow visualization at 30% sampling rate
    • Table 1. PSNR of reconstructed images of different algorithms in 8 pictures

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      Table 1. PSNR of reconstructed images of different algorithms in 8 pictures

      采样率算法MonarchHouseBarbaraLenaParrotsPeppersBoatsC.man
      10%MH2223.1930.3026.7326.1225.3526.0026.1122.12
      ReconNet2321.5126.6922.5024.4723.2322.6724.1521.66
      CSNet926.7331.6824.2428.5727.4026.6628.8024.92
      ISTA-Net+[2425.7230.4923.5227.5026.3727.1327.4123.76
      G2LNet28.0732.0025.2328.8628.8827.6128.9525.46
      20%MH2227.1033.8430.8129.8129.2129.7829.9125.87
      ReconNet2322.8927.9522.8725.3924.5624.0425.9822.64
      CSNet929.5733.4224.9830.7529.7728.4230.9726.79
      ISTA-Net+[2431.0134.9926.7831.1429.9630.4531.9127.65
      G2LNet31.6135.1627.2731.7131.1231.3832.4428.30
      30%MH2229.2035.6932.9931.9931.0031.3532.2528.08
      ReconNet2329.2133.6125.6533.7426.8829.7730.2026.90
      CSNet932.5836.4728.3833.3532.8730.7533.2128.64
      ISTA-Net+[2434.8037.0730.1333.4532.9135.5435.2230.35
      G2LNet35.2737.5630.8734.2633.5835.8135.8731.28
    • Table 2. PSNR of reconstructed images of different algorithms in multiple datasets

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      Table 2. PSNR of reconstructed images of different algorithms in multiple datasets

      采样率数据集MH22ReconNet23CSNet9ISTA-Net+[24G2LNet
      10%Set528.5624.3131.5428.6130.56
      Set1125.8222.4527.3726.4927.95
      BSD6823.9723.6226.1225.3026.66
      Avg.26.1223.4628.3426.8028.39
      20%Set526.1223.4628.3426.8028.39
      Set1128.9324.4429.3330.8031.21
      BSD6826.9825.1229.5129.0329.59
      Avg.27.3424.3429.0628.8829.73
      30%Set534.0927.7836.6935.4536.03
      Set1131.5525.7430.9833.7033.71
      BSD6828.7926.2731.0130.3531.29
      Avg.31.4826.6032.8933.1733.68
    • Table 3. Comparison results of SSIM of reconstructed images with different algorithms

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      Table 3. Comparison results of SSIM of reconstructed images with different algorithms

      采样率数据集MH22ReconNet23CSNet9ISTA-Net+[24G2LNet
      10%Set50.835 00.734 10.901 00.839 80.898 6
      Set110.782 20.624 50.841 20.803 60.868 7
      BSD680.655 90.648 20.772 40.700 10.789 6
      Avg.0.757 70.668 90.838 20.781 20.852 3
      20%Set50.888 10.794 00.950 10.902 10.943 6
      Set110.872 10.732 40.901 20.913 10.922 5
      BSD680.769 30.705 20.875 70.864 20.882 0
      Avg.0.843 20.743 90.909 00.893 10.916 0
      30%Set50.918 50.823 30.963 30.934 10.959 3
      Set110.906 30.804 60.891 20.938 20.950 0
      BSD680.827 60.784 20.882 40.878 20.918 6
      Avg.0.884 10.804 00.912 30.916 80.942 6
    • Table 4. Comparing results with the same structured model without convolutional filter flow

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      Table 4. Comparing results with the same structured model without convolutional filter flow

      数据集算法模型采样率
      10%20%30%
      PSNRSSIMPSNRSSIMPSNRSSIM
      Set5FFCNet22.160.788 922.820.825 623.200.842 6
      G2LNet30.560.898 634.110.943 636.030.959 3
      Set11FFCNet23.980.825 324.290.869 726.720.904 6
      G2LNet27.950.868 731.210.922 533.710.950 0
      BSD68FFCNet22.670.734 623.640.813 425.110.854 1
      G2LNet28.390.789 629.590.882 031.290.918 6
    • Table 5. Sampling rate is 10%, Comparison of the average running time of different algorithms in Set11

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      Table 5. Sampling rate is 10%, Comparison of the average running time of different algorithms in Set11

      算法名称平均运行时间(CPU/GPU)
      MH2222.703 468 91/-
      ReconNet23-/0.052 174 221
      CSNet9-/0.049 654 527
      ISTA-Net+24-/0.009 718 182
      G2LNet-/0.123 254 545
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    Yuanhong ZHONG, Qianfeng XU, Yujie ZHOU, Shanshan WANG. Image reconstruction based on deep compressive sensing combined with global and local features[J]. Optics and Precision Engineering, 2023, 31(14): 2135

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    Paper Information

    Category: Information Sciences

    Received: Dec. 6, 2022

    Accepted: --

    Published Online: Aug. 2, 2023

    The Author Email: ZHONG Yuanhong (zhongyh@cqu.edu.cn)

    DOI:10.37188/OPE.20233114.2135

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