杂志上的传感器
研究文章

基于特征的图像超分辨率网络融合的注意

表2

基准测试结果,平均PSNR值/ SSIM,大胆是最好的结果,和斜体是第二个最好的结果。
方法 Set14 BSD100 Urban100
X2 X3 X4 X2 X3 X4 X2 X3 X4
双三次的 30.240 27.550 26.000 29.560 27.210 25.960 26.880 24.460 23.140
0.869 0.774 0.703 0.843 0.739 0.668 0.840 0.735 0.658
SRCNN 32.450 29.300 27.500 31.360 28.410 26.900 29.500 26.240 24.520
0.907 0.822 0.751 0.888 0.786 0.710 0.895 0.799 0.722
LapSRN 33.080 29.790 28.190 31.800 28.820 27.320 30.410 27.070 25.210
0.913 0.832 0.772 0.895 0.797 0.728 0.910 0.827 0.755
SRDenseNet - - - - - - - - - - - - 28.500 - - - - - - - - - - - - 27.530 - - - - - - - - - - - - 26.050
- - - - - - - - - - - - 0.778 - - - - - - - - - - - - 0.734 - - - - - - - - - - - - 0.782
肉 33.520 30.290 28.600 32.090 29.060 27.580 31.920 28.060 26.070
0.917 0.841 0.781 0.898 0.803 0.735 0.926 0.849 0.784
EDSR 33.920 30.520 28.800 32.320 29.250 27.710 32.930 28.800 26.640
0.920 0.846 0.788 0.901 0.809 0.742 0.935 0.865 0.803
RDN 34.010 30.570 28.810 32.340 29.260 27.720 32.890 28.800 26.610
0.921 0.847 0.787 0.902 0.809 0.742 0.935 0.865 0.803
SwinSR 33.070 32.182 31.091 33.345 28.900 31.474 33.856 28.793 24.525
0.891 0.889 0.847 0.933 0.825 0.854 0.959 0.874 0.781
DeFiAN 33.789 33.747 31.087 32.955 29.050 31.448 34.196 28.537 25.050
0.908 0.913 0.847 0.931 0.830 0.870 0.959 0.866 0.797
RDAN 35.815 33.881 31.228 33.281 30.133 29.646 34.289 28.816 26.473
0.923 0.908 0.850 0.939 0.859 0.838 0.958 0.885 0.827