# Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
## 4 Experiments
### Comparative Experiment
#### 4.3.1 In-Distribution  
Table 1 shows the PSNR index of each algorithm's \(\times\)2, \(\times\)3, and \(\times\)4 fixed multiples. It can be seen that the RDST in this paper achieved the best performance. Compared with the previous classic neural network algorithms SRCNN, DRRN, and SRDenseNet, RDST shows powerful feature extraction capabilities.  
Figure 2 shows the visual effects of the algorithm in this paper and the classic SR algorithms with for super-resolution and fixed-size images. Figure 1 shows the results for four times, three times, and two times scale factors from top to bottom. For the \"img078\" in Urban100 and the \"zebra\" in Set14, the super-segmentation result of RDST preserves the texture details in the image. Compared with the other methods, it has fewer artifacts and is more suitable for human perception. For the \"bird\" in Set5, our super-score results are also  
\begin{table}
\begin{tabular}{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**} \\  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Bicubic & \(\times\)2 & 33.67 & 0.9299 & 30.32 & 0.8688 & 29.55 & 0.8431 & 26.87 & 0.8403 & 30.82 & 0.9339 \\ SRCNN & \(\times\)2 & 36.66 & 0.9542 & 32.45 & 0.9067 & 31.36 & 0.8879 & 29.5 & 0.8946 & 35.6 & 0.9663 \\ DRRN & \(\times\)2 & 37.74 & 0.9591 & 33.23 & 0.9136 & 32.05 & 0.8973 & 31.23 & 0.9188 & 37.6 & 0.9736 \\ SRDenseNet & \(\times\)2 & -/- & -/- & -/- & -/- & -/- & -/- & -/- & -/- & -/- & -/- \\ EDSR & \(\times\)2 & 38.11 & 0.9602 & 33.92 & 0.9195 & 32.32 & 0.9013 & 32.93 & 0.9351 & 39.1 & 0.9773 \\ RCAN & \(\times\)2 & 38.27 & 0.9614 & 34.12 & 0.9216 & 32.41 & 0.9027 & 33.34 & 0.9384 & 39.44 & 0.9786 \\ RDST-s* & \(\times\)2 & 38.32 & 0.9617 & 34.41 & 0.9243 & 32.44 & 0.9025 & 33.4 & 0.9398 & 39.73 & 0.9793 \\ \hline Bicubic & \(\times\)3 & 30.4 & 0.8682 & 27.63 & 0.7742 & 27.2 & 0.7385 & 24.45 & 0.7349 & 26.95 & 0.8556 \\ SRCNN & \(\times\)3 & 32.75 & 0.909 & 29.3 & 0.8215 & 28.41 & 0.7863 & 26.24 & 0.7989 & 30.48 & 0.9117 \\ DRRN & \(\times\)3 & 34.03 & 0.9244 & 29.96 & 0.8349 & 28.95 & 0.8004 & 27.53 & 0.8378 & 32.42 & 0.9359 \\ SRDenseNet & \(\times\)3 & -/- & -/- & -/- & -/- & -/- & -/- & -/- & -/- & -/- & -/- \\ EDSR & \(\times\)3 & 34.65 & 0.928 & 30.52 & 0.8462 & 29.25 & 0.8093 & 28.8 & 0.8653 & 34.17 & 0.9476 \\ RCAN & \(\times\)3 & 34.74 & 0.9299 & 30.65 & 0.8482 & 29.32 & 0.8111 & 29.09 & 0.8702 & 34.44 & 0.9499 \\ RDST-s* & \(\times\)3 & 34.82 & 0.9304 & 30.77 & 0.8501 & 29.36 & 0.812 & 29.28 & 0.8742 & 34.82 & 0.9519 \\ \hline Bicubic & \(\times\)4 & 28.43 & 0.8104 & 26.09 & 0.7027 & 25.95 & 0.6675 & 23.14 & 0.6577 & 24.9 & 0.7866 \\ SRCNN & \(\times\)4 & 30.48 & 0.8628 & 27.5 & 0.7513 & 26.9 & 0.7101 & 24.52 & 0.7221 & 27.58 & 0.8555 \\ DRRN & \(\times\)4 & 31.68 & 0.8888 & 28.21 & 0.7721 & 27.38 & 0.7284 & 25.44 & 0.7638 & 29.18 & 0.8914 \\ SRDenseNet & \(\times\)4 & 32.02 & 0.8934 & 28.5 & 0.7782 & 27.53 & 0.7337 & 26.05 & 0.7819 & -/- & -/- \\ EDSR & \(\times\)4 & 32.46 & 0.8968 & 28.8 & 0.7876 & 27.71 & 0.742 & 26.64 & 0.8033 & 31.02 & 0.9148 \\ RCAN & \(\times\)4 & 32.63 & 0.9002 & 28.87 & 0.7889 & 27.77 & 0.7436 & 26.82 & 0.8087 & 31.22 & 0.9173 \\ RDST-s* & \(\times\)4 & 32.66 & 0.9013 & 28.99 & 0.791 & 27.82 & 0.745 & 27.07 & 0.8147 & 31.8 & 0.9232 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with classical SISR methods. Best and second best performance are in red and blue colors, respectively.  
very close to the original HR results. The good visual effects show that RDST makes full use of multilevel features and Transformer's global modeling capabilities.