# Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
## 4 Results
### Comparison with State-of-the-Art Methods
The specific comparison results are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**Set5**} & \multicolumn{2}{c}{**Set14**} & \multicolumn{2}{c}{**B100**} & \multicolumn{2}{c}{**Urban100**} & \multicolumn{2}{c}{**Manga109**} \\ \cline{3-11}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline BICUBIC & \(\times\)2 & 33.66 & 0.9299 & 30.24 & 0.8688 & 29.56 & 0.8431 & 26.88 & 0.8403 & 30.80 & 0.9339 \\ \hline A+ & \(\times\)2 & 36.60 & 0.9542 & 32.42 & 0.9059 & 31.24 & 0.8870 & 29.25 & 0.8955 & 35.37 & 0.9663 \\ \hline SRCNN [5] & \(\times\)2 & 36.66 & 0.9542 & 32.45 & 0.9067 & 31.36 & 0.8879 & 29.50 & 0.8946 & 35.60 & 0.9663 \\ \hline VDSR [8] & \(\times\)2 & 37.53 & 0.9590 & 33.05 & 0.9130 & 31.90 & 0.8960 & 30.77 & 0.9140 & 37.22 & 0.9750 \\ \hline Lap-SRN [10] & \(\times\)2 & 37.52 & 0.9591 & 33.08 & 0.9130 & 31.08 & 0.8950 & 30.41 & 0.9101 & 37.27 & 0.9740 \\ \hline EDSR-baseline [9] & \(\times\)2 & 37.99 & 0.9604 & 33.57 & 0.9175 & 32.16 & 0.8994 & 31.98 & 0.9272 & 38.54 & 0.9769 \\ \hline CARN [39] & \(\times\)2 & 37.76 & 0.9590 & 33.52 & 0.9166 & 32.09 & 0.8978 & 31.92 & 0.9256 & 38.36 & 0.9765 \\ \hline IDN [40] & \(\times\)2 & 37.83 & 0.9600 & 33.30 & 0.9148 & 32.08 & 0.8985 & 31.27 & 0.9196 & 38.01 & 0.9749 \\ \hline MSRN [12] & \(\times\)2 & 38.08 & 0.9605 & 33.74 & 0.9170 & 32.23 & 0.9002 & 32.22 & 0.9326 & 38.82 & 0.9772 \\ \hline MSCIF [42] & \(\times\)2 & 37.89 & 0.9605 & 33.41 & 0.9153 & 32.15 & 0.8892 & 31.47 & 0.9220 & 0.920 & 0.9770 \\ \hline MSFRN [13] & \(\times\)2 & 38.02 & 0.9606 & 33.68 & 0.9184 & 32.19 & 0.8998 & 32.17 & 0.9287 & 38.59 & 0.9770 \\ \hline MIPN [41] & \(\times\)2 & 38.12 & _0.9610_ & 33.73 & 0.9180 & 32.25 & 0.9006 & 32.42 & 0.9310 & _38.88_ & _0.9773_ \\ \hline MSAR [14] & \(\times\)2 & **38.22** & **0.9616** & **33.79** & _0.9189_ & **32.27** & **0.9108** & 32.46 & 0.9322 & 0.9322 & 0.9326 \\ \hline MCFN & \(\times\)2 & 38.17 & _0.9610_ & **33.95** & **0.9211** & **32.29** & _0.9011_ & **32.80** & **0.9345** & **38.93** & **0.9775** \\ \hline BICUBIC & \(\times\)3 & 30.39 & 0.8682 & 27.55 & 0.7742 & 27.21 & 0.7385 & 24.46 & 0.7349 & 26.95 & 0.8556 \\ \hline A+ & \(\times\)3 & 32.63 & 0.9085 & 29.25 & 0.8194 & 28.31 & 0.7828 & 26.05 & 0.8019 & 29.93 & 0.9089 \\ \hline SRCNN [5] & \(\times\)3 & 32.75 & 0.9090 & 29.30 & 0.8215 & 28.41 & 0.7863 & 26.24 & 0.7989 & 30.48 & 0.9117 \\ \hline VDSR [8] & \(\times\)3 & 33.67 & 0.9210 & 29.78 & 0.8320 & 28.83 & 0.7990 & 27.14 & 0.8290 & 32.01 & 0.9340 \\ \hline Lap-SRN [10] & \(\times\)3 & 33.82 & 0.9227 & 29.87 & 0.8320 & 28.82 & 0.7980 & 27.07 & 0.8280 & 32.21 & 0.9350 \\ \hline EDSR-baseline [9] & \(\times\)3 & 34.37 & 0.9270 & 30.28 & 0.8417 & 29.09 & 0.8052 & 28.15 & 0.8527 & 33.45 & 0.9439 \\ \hline CARN [39] & \(\times\)3 & 34.29 & 0.9255 & 30.29 & 0.8407 & 29.06 & 0.8034 & 28.06 & 0.8493 & 33.50 & 0.9440 \\ \hline IDN [40] & \(\times\)3 & 34.11 & 0.9253 & 29.99 & 0.8354 & 28.95 & 0.8013 & 27.42 & 0.8359 & 32.71 & 0.9381 \\ \hline MSRN [12] & \(\times\)3 & 34.38 & 0.9262 & 30.34 & 0.8395 & 29.08 & 0.8041 & 28.08 & 0.8554 & 33.44 & 0.9427 \\ \hline MSCIF [42] & \(\times\)3 & 34.24 & 0.9266 & 30.09 & 0.8371 & 29.01 & 0.8024 & 27.69 & 0.8411 & 0.9427 \\ \hline MSFRN [13] & \(\times\)3 & 34.40 & 0.9272 & 30.34 & 0.8423 & 29.10 & 0.8052 & 28.19 & 0.8530 & 33.59 & 0.9447 \\ \hline MIPN [41] & \(\times\)3 & 34.53 & 0.9280 & 30.43 & 0.8440 & 29.15 & _0.8060_ & 28.38 & 0.8570 & 33.86 & 0.9460 \\ \hline MSAR [14] & \(\times\)3 & _34.59_ & _0.9285_ & **30.53** & _0.8446_ & **29.35** & 0.8044 & _28.44_ & _0.8586_ & **33.98** & **0.9470** \\ \hline MCFN & \(\times\)3 & **34.63** & **0.9289** & **30.53** & **0.8458** & _29.29_ & **0.8087** & **28.73** & **0.8635** & **33.98** & _0.9469_ \\ \hline BICUBIC & \(\times\)4 & 28.43 & 0.8020 & 26.10 & 0.6940 & 25.96 & 0.6600 & 23.150 & 0.6590 & 21.460 & 0.6138 \\ \hline A+ & \(\times\)4 & 30.33 & 0.8560 & 27.44 & 0.7450 & 26.83 & 0.7000 & 24.340 & 0.7210 & 22.390 & 0.6454 \\ \hline SRCNN [5] & \(\times\)4 & 30.48 & 0.8630 & 27.49 & 0.7500 & 26.90 & 0.7100 &It can be observed in these results that the MCFN network shows a significant advantage in most of the performance metrics compared to the recently proposed methods.