Author Affiliations
College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, Chinashow less
Fig. 1. Structure of ESPCN network
Fig. 2. Structure of VDSR network
Fig. 3. Network structure of proposed algorithm
Fig. 4. Recursive unit module. (a) Recursive unit module composition; (b) residual channel attention; (c) multilevel feature fusion
Fig. 5. Variation of mean PSNR with the number of iterations for different layers at Set5 test set
Fig. 6. Relationship between number of parameters of different network structures and mean PSNR at Set5 test set
Fig. 7. Relationship between run time of different methods and mean PSNR at Set5 test set
Fig. 8. Comparison of zebra image recovered with different algorithms
Fig. 9. Comparison of ppt image recovered with different algorithms
Multilevelfeature fusion | Residualchannel attention | PSNR |
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√ | √ | 31.61 | × | √ | 31.35 | √ | × | 31.40 | × | × | 30.94 |
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Table 1. Means PSNR of different RCAF model components at Set 5 test set
Test set | Scale | Bicubic | SRCNN[8] | ESPCN[10] | FSRCNN[9] | VDSR[11] | RCAF |
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| 2 | 33.68 | 36.19 | 36.38 | 36.45 | 37.34 | 37.62 | Set5 | 3 | 30.45 | 32.46 | 32.71 | 32.59 | 33.47 | 34.00 | | 4 | 28.46 | 30.15 | 30.29 | 30.42 | 30.78 | 31.61 | | 2 | 30.21 | 32.10 | 32.20 | 32.21 | 32.82 | 33.24 | Set14 | 3 | 27.51 | 28.99 | 29.12 | 29.12 | 29.51 | 29.87 | | 4 | 25.98 | 27.23 | 27.17 | 27.43 | 27.62 | 28.11 | | 2 | 29.43 | 30.88 | 30.93 | 31.24 | 31.51 | 31.81 | BSD100 | 3 | 27.08 | 28.06 | 28.16 | 28.25 | 28.43 | 28.72 | | 4 | 25.84 | 26.63 | 26.59 | 26.85 | 26.87 | 27.18 |
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Table 2. Mean PSNR of different algorithms at Set5, Set14, and BSD100 test sets
Dataset | Scale | Bicubic | SRCNN[8] | ESPCN[10] | FSRCNN[9] | VDSR[11] | RCAF |
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| 2 | 0.931 | 0.955 | 0.957 | 0.957 | 0.958 | 0.963 | Set5 | 3 | 0.869 | 0.911 | 0.915 | 0.912 | 0.919 | 0.930 | | 4 | 0.810 | 0.862 | 0.863 | 0.866 | 0.875 | 0.893 | | 2 | 0.869 | 0.958 | 0.960 | 0.963 | 0.910 | 0.964 | Set14 | 3 | 0.774 | 0.884 | 0.887 | 0.892 | 0.827 | 0.897 | | 4 | 0.702 | 0.821 | 0.823 | 0.827 | 0.759 | 0.842 | | 2 | 0.844 | 0.880 | 0.883 | 0.887 | 0.892 | 0.897 | BSD100 | 3 | 0.740 | 0.776 | 0.781 | 0.780 | 0.793 | 0.797 | | 4 | 0.670 | 0.692 | 0.694 | 0.701 | 0.719 | 0.720 |
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Table 3. Mean SSIM of different algorithms at test sets Set5, Set14, and BSD100