Journal of Applied Optics, Volume. 43, Issue 1, 52(2022)
Reconstruction method of computational ghost imaging based on non-local generalized total variation
Figures & Tables(13)
Fig. 1. Schematic diagram of computational ghost imaging measurement for reflective detectors
Fig. 2. Visual comparison results of different algorithms for simulation 1
Fig. 3. Visual comparison results of different algorithms for simulation 2
Fig. 4. Visual comparison results of different algorithms for simulation 3
Fig. 5. Visual comparison results of different algorithms for simulation 4
Fig. 6. Systematic diagram of computational ghost imaging measurement for reflective detectors
Fig. 9. Visual comparison results of different algorithms for experiment 1
Fig. 10. Visual comparison results of different algorithms for experiment 2
Fig. 11. Visual comparison results of different algorithms for experiment 3
Table 1. Pseudo code of NLGTV image reconstruction algorithm
Table 2. Comparison results of two evaluation indicators for different algorithms
View tableView in ArticleTable 2. Comparison results of two evaluation indicators for different algorithms
仿真类型 指标 DGI GD CGD AP Sparse TV Proposed 仿真1 PSNR/dB 19.666 28.885 29.785 29.685 29.786 38.383 39.919 SSIM 0.051 0.527 0.547 0.529 0.547 0.901 0.936 仿真2 PSNR/dB 20.775 30.038 30.84 30.7 30.848 40.18 41.544 SSIM 0.027 0.65 0.699 0.692 0.701 0.919 0.942 仿真3 PSNR/dB 19.65 30.512 44.81 37.694 40.92 43.503 47.537 SSIM 0.054 0.627 0.969 0.867 0.93 0.962 0.987 仿真4 PSNR/dB 20.648 31.121 31.723 31.438 31.744 38.346 39.25 SSIM 0.064 0.621 0.642 0.628 0.649 0.917 0.946
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Zonghua JIANG, Xin TIAN, Jinling YANG. Reconstruction method of computational ghost imaging based on non-local generalized total variation[J]. Journal of Applied Optics, 2022, 43(1): 52
Paper Information
Category: OE INFORMATION ACQUISITION AND PROCESSING
Received: Aug. 5, 2021
Accepted: --
Published Online: Mar. 7, 2022
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