# Residual Dense Swin Transformer for Continuous-Scale Super-Resolution Algorithm
## 4 Experiments
### Comparative Experiment
#### 4.3.2 Out of Distribution  
Different from the ordinary fixed magnification SR method, our proposed RDST can achieve super-resolution effects for any multiple with the help of LIIF. In order to further explore the combination capabilities of different encoders with LIIF, CNN-based models were selected, including EDSR baseline (EDSR\({}_{(b)}\)) and RDN, and compared with the proposed RDST. RDST-t, RDST-s, and RDST-b refer to the tiny, small, and base versions of RDST, respectively. The number of RDTBs and the number of STLs in the block were four, six, and eight, respectively.  
As Table 2 shows, RDST-s and RDST-b almost captured the best PSNR indicators for each scale. Especially for the PSNR index outside the distribution, our method is generally 0.1\(\sim\)0.6 dB higher than the model based on CNN combined with LIIF. This finding fully proves the excellent generalization ability of RDST and the powerful feature extraction ability of the RDTB that we designed. The powerful extra-distribution performance is also due to the combination of Transformer's unique encoding and LIIF for continuous image expression. Figures 3-5, respectively, show the visual effects of 6 times, 18 times, and 30 times the super score. It can be clearly seen from the figures that our proposed RDST can also achieve good visual effects even at multiples outside of the hyperdivision distribution. Compared with other methods, RDST can better retain texture details such as \"glass boundary\" and \"railing shape\", retain more high-frequency details of the image, and produce super-resolution high-quality images that are more suitable for the human eye.  
Figure 2: Visual results with a scale factor of 4, 3, and 2.  
Figure 4: Visual results with a scale factor of 18.  
Figure 5: Visual results with a scale factor of 18.  
Figure 3: Visual results with a scale factor of 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Metric**} & \multicolumn{6}{c}{**In Distribution**} & \multicolumn{6}{c}{**Out of Distribution**} \\ \cline{3-11}  & & & \(\times\)**2** & \(\times\)**3** & \(\times\)**4** & \(\times\)**6** & \(\times\)**8** & \(\times\)**12** & \(\times\)**18** & \(\times\)**24** & \(\times\)**30** \\ \hline \multirow{8}{*}{Manga109} & bicubic & PSNR & 30.82 & 26.95 & 24.90 & 22.69 & 21.45 & 19.98 & 18.76 & 17.99 & 17.46 \\  & SSIM & 0.9339 & 0.8556 & 0.7866 & 0.6958 & 0.6460 & 0.5977 & 0.5722 & 0.5624 & 0.5571 \\  & Dense-LIIF & PSNR & 38.12 & 32.93 & 29.96 & 26.22 & 24.14 & 21.77 & 19.97 & 18.93 & 18.25 \\  & DSIM & 0.9757 & 0.9407 & 0.9018 & 0.8242 & 0.7638 & 0.6830 & 0.6209 & 0.5909 & 0.5744 \\  & EDSR(b)-LIIF & PSNR & 38.67 & 33.53 & 30.58 & 26.77 & 24.57 & 22.04 & 20.14 & 19.06 & 18.34 \\  & SSIM & 0.9770 & 0.9450 & 0.9096 & 0.8380 & 0.7791 & 0.6954 & 0.6287 & 0.5954 & 0.5768 \\  & RDN-LIIF & PSNR & 39.26 & 34.21 & 31.20 & 27.33 & 25.04 & 22.36 & 20.35 & 19.20 & 18.44 \\  & SRIM & 0.9781 & 0.9487 & 0.9170 & 0.8508 & 0.7948 & 0.7099 & 0.6386 & 0.6014 & 0.5806 \\  & RDST-t & PSNR & 39.06 & 33.99 & 31.00 & 27.10 & 24.86 & 22.35 & 20.46 & 19.37 & 18.44 \\  & SSIM & 0.9779 & 0.9475 & 0.9151 & 0.8463 & 0.7894 & 0.7084 & 0.6412 & 0.6057 & 0.5810 \\  & RDST-s & PSNR & 39.33 & 34.32 & 31.33 & 27.44 & 25.16 & 25.27 & 20.61 & 19.49 & 18.53 \\  & RDST-s & SSIM & 0.9784 & 0.9496 & 0.9189 & 0.8532 & 0.7980 & 0.7165 & 0.6470 & 0.6100 & 0.5837 \\  & PSNR & 39.39 & 34.42 & 31.45 & 27.53 & 25.23 & 22.62 & 20.65 & 19.51 & 18.55 \\  & RDST-b & SSIM & 0.9785 & 0.9500 & 0.9198 & 0.8545 & 0.7997 & 0.7188 & 0.6490 & 0.6110 & 0.5844 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._