Fig. 1. Network architecture of MAAN
Fig. 2. Architecture of BU
Fig. 3. Architecture of ADB
Fig. 4. Architecture of AWU
Fig. 5. Architecture of MPIB
Fig. 6. Architecture of ADA
Fig. 7. Comparison of the visual effect of different attention
Fig. 8. Comparison of visual effect of each method with a scale factor of ×4
数量 | 6 | 7 | 8 | 9 |
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参数量/M | 7.36 | 8.52 | 9.61 | 10.83 | PSNR | 38.01 | 38.20 | 38.37 | 38.40 |
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Table 1. Ablation experiments of the numbers of MFFB
结构 | Set5 | Set14 | BSD100 | Urban100 |
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PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM |
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结构1 | 32.35 | 0.896 3 | 28.66 | 0.782 1 | 27.45 | 0.727 4 | 26.58 | 0.799 8 | 结构2 | 32.39 | 0.895 9 | 28.78 | 0.787 6 | 27.42 | 0.729 4 | 26.64 | 0.803 3 | 结构3 | 32.43 | 0.899 1 | 28.85 | 0.798 2 | 27.54 | 0.731 9 | 26.59 | 0.802 8 | 结构4 | 32.69 | 0.900 2 | 29.03 | 0.810 5 | 27.74 | 0.745 1 | 26.92 | 0.816 7 | 结构5 | 32.76 | 0.901 8 | 29.11 | 0.811 8 | 27.83 | 0.746 3 | 27.03 | 0.819 5 | 结构6 | 32.77 | 0.902 0 | 29.12 | 0.812 0 | 27.85 | 0.746 9 | 27.08 | 0.819 4 | 完整的BU | 32.79 | 0.902 3 | 29.15 | 0.812 8 | 27.87 | 0.747 3 | 27.12 | 0.820 9 |
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Table 2. Results of ablation experiments of the structure of BU
分支 | 3×3 | 5×5 | 7×7 | 3×3+5×5 | 3×3+5×5+7×7 | 3×3+5×5+AWU |
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参数量/M | 3.21 | 5.84 | 8.53 | 9.50 | 19.99 | 9.69 | PSNR | 34.26 | 34.37 | 34.43 | 34.81 | 34.95 | 34.91 |
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Table 3. Results of ablation experiments of the structure of ADB
组数 | 2 | 4 | 8 |
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参数量/M | 7.79 | 9.61 | 14.55 | PSNR | 37.82 | 38.37 | 38.53 |
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Table 4. Ablation experiments of the groups of MPIB
方法 | 参数量/M | Set5 | Set14 | BSD100 | Urban100 |
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PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM |
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基础网络 | 9.65 | 34.66 | 0.928 8 | 30.52 | 0.847 4 | 29.12 | 0.809 7 | 28.91 | 0.872 3 | +ECA | 9.67 | 34.72 | 0.929 8 | 30.67 | 0.848 3 | 29.21 | 0.811 0 | 29.01 | 0.872 4 | +CBAM | 9.73 | 34.78 | 0.930 1 | 30.70 | 0.848 7 | 29.26 | 0.811 4 | 29.09 | 0.873 1 | +BAM | 9.78 | 34.76 | 0.929 9 | 30.72 | 0.849 3 | 29.22 | 0.811 5 | 29.18 | 0.873 6 | +ADA | 9.69 | 34.93 | 0.931 7 | 30.80 | 0.850 9 | 29.40 | 0.813 6 | 29.37 | 0.875 3 |
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Table 5. Comparison of the evaluation metrics of different attention
比例 | 方法 | 参数量/M | Set5 | Set14 | BSD100 | Urban100 |
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PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM |
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×3 | EDSR | 43.7 | 34.65 | 0.928 0 | 30.52 | 0.846 2 | 29.25 | 0.809 3 | 28.80 | 0.865 3 | | RDN | 22.3 | 34.71 | 0.929 6 | 30.57 | 0.846 8 | 29.26 | 0.809 3 | 28.80 | 0.865 3 | | E-GSCN | 15.9 | 34.69 | 0.929 4 | 30.59 | 0.847 1 | 29.28 | 0.809 9 | 28.87 | 0.866 9 | | DRLN | 33.7 | 34.78 | 0.930 3 | 30.73 | 0.848 8 | 29.36 | 0.811 7 | 29.21 | 0.872 2 | | SwinIR | 11.9 | 34.89 | 0.931 2 | 30.77 | 0.850 3 | 29.37 | 0.812 4 | 29.29 | 0.874 4 | | MAAN | 9.69 | 34.93 | 0.931 7 | 30.80 | 0.850 9 | 29.40 | 0.813 6 | 29.37 | 0.875 3 | | Bicubic | - | 28.43 | 0.802 2 | 26.10 | 0.693 6 | 25.97 | 0.651 7 | 23.14 | 0.659 9 | | SRCNN | 0.02 | 30.48 | 0.862 8 | 27.49 | 0.750 3 | 26.90 | 0.710 1 | 24.52 | 0.722 1 | | VDSR | 0.67 | 31.35 | 0.883 8 | 28.01 | 0.767 4 | 27.29 | 0.725 1 | 25.18 | 0.752 4 | | DRRN | 0.30 | 31.68 | 0.888 8 | 28.21 | 0.772 0 | 27.38 | 0.728 4 | 25.44 | 0.763 8 | | A2F-L | 1.37 | 32.32 | 0.896 4 | 28.67 | 0.783 9 | 27.62 | 0.737 9 | 36.32 | 0.793 1 | ×4 | JTF-SISR | 18.5 | 32.53 | 0.898 9 | 28.80 | 0.786 7 | 27.70 | 0.741 0 | 26.62 | 0.802 2 | | EDSR | 43.1 | 32.46 | 0.896 8 | 28.80 | 0.787 6 | 27.71 | 0.742 0 | 26.64 | 0.803 3 | | RDN | 22.6 | 32.47 | 0.899 0 | 28.81 | 0.787 1 | 27.72 | 0.741 9 | 26.61 | 0.802 8 | | E-GSCN | 15.9 | 32.53 | 0.899 2 | 28.84 | 0.787 9 | 27.74 | 0.742 4 | 26.71 | 0.805 0 | | DRLN | 34.0 | 32.63 | 0.900 2 | 28.94 | 0.790 0 | 27.83 | 0.744 4 | 26.98 | 0.811 9 | | SwinIR | 11.9 | 32.75 | 0.902 1 | 28.94 | 0.791 4 | 27.83 | 0.745 9 | 27.07 | 0.816 4 | | MAAN | 9.72 | 32.79 | 0.902 3 | 29.15 | 0.812 8 | 27.87 | 0.747 3 | 27.12 | 0.820 9 |
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Table 6. Comparison of evaluation metrics of each method with scale factors of ×2, ×3, ×4
比例 | 方法 | 参数量/M | Set5 | Set14 | BSD100 | Urban100 |
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PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM |
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| Bicubic | - | 33.69 | 0.928 4 | 30.34 | 0.867 5 | 29.57 | 0.843 4 | 26.88 | 0.843 8 | | SRCNN | 0.02 | 36.66 | 0.954 2 | 32.42 | 0.906 3 | 31.36 | 0.887 9 | 29.50 | 0.894 6 | | VDSR | 0.67 | 37.53 | 0.958 7 | 33.03 | 0.912 4 | 31.90 | 0.896 0 | 30.76 | 0.914 0 | | DRRN | 0.30 | 37.74 | 0.959 1 | 33.23 | 0.913 6 | 32.05 | 0.897 3 | 31.23 | 0.918 8 | | A2F-L | 1.36 | 38.09 | 0.960 7 | 33.78 | 0.919 2 | 32.23 | 0.900 2 | 32.46 | 0.931 3 | | JTF-SISR | 17.8 | 38.24 | 0.961 3 | 33.83 | 0.919 6 | 32.34 | 0.901 7 | 32.81 | 0.934 9 | ×2 | EDSR | 40.7 | 38.11 | 0.960 2 | 33.92 | 0.919 5 | 32.32 | 0.901 3 | 32.93 | 0.935 1 | | RDN | 22.1 | 38.24 | 0.961 4 | 34.01 | 0.921 2 | 32.34 | 0.901 7 | 32.89 | 0.935 3 | | E-GSCN | 15.7 | 38.21 | 0.961 2 | 33.95 | 0.920 3 | 32.35 | 0.901 9 | 32.94 | 0.935 7 | | DRLN | 32.4 | 38.27 | 0.961 6 | 34.28 | 0.923 1 | 32.44 | 0.902 8 | 33.37 | 0.939 0 | | SwinIR | 11.8 | 38.35 | 0.962 0 | 34.14 | 0.922 7 | 32.44 | 0.903 0 | 33.40 | 0.939 3 | | MAAN | 9.61 | 38.37 | 0.962 6 | 34.26 | 0.923 8 | 32.49 | 0.903 2 | 33.43 | 0.940 1 | | Bicubic | - | 30.41 | 0.865 5 | 27.64 | 0.772 2 | 27.21 | 0.734 4 | 24.46 | 0.741 1 | | SRCNN | 0.02 | 32.75 | 0.909 0 | 29.28 | 0.820 9 | 28.41 | 0.786 3 | 26.24 | 0.798 9 | | VDSR | 0.67 | 33.66 | 0.921 3 | 29.77 | 0.831 4 | 28.82 | 0.797 6 | 27.14 | 0.827 9 | | DRRN | 0.30 | 34.03 | 0.924 4 | 29.96 | 0.834 9 | 28.95 | 0.800 4 | 27.53 | 0.837 8 | | A2F-L | 1.37 | 34.54 | 0.928 3 | 30.41 | 0.843 6 | 29.14 | 0.806 2 | 28.40 | 0.857 4 | | JTF-SISR | 18.1 | 34.72 | 0.929 5 | 30.57 | 0.846 5 | 29.25 | 0.809 0 | 28.74 | 0.864 5 |
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Table 6. Comparison of evaluation metrics of each method with scale factors of ×2, ×3, ×4
方法 | PSNR | SSIM | 时间 /s | 参数量/M | 计算量/G |
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EDSR | 27.71 | 0.742 0 | 0.338 | 43.1 | 516 | RDN | 27.72 | 0.741 9 | 0.224 | 22.6 | 209 | E-GSCN | 27.74 | 0.742 4 | 0.136 | 15.9 | 144 | DRLN | 27.83 | 0.744 4 | 0.271 | 34.0 | 292 | SwinIR | 27.83 | 0.745 9 | 0.112 | 11.9 | 128 | MAAN | 27.87 | 0.747 3 | 0.085 | 9.72 | 79 |
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Table 7. Complexity comparison of the 3 methods