# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 4 Experiments
### Quantitative and Qualitative Comparison of Different Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Test**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Bicubic**} & **ESRGAN** & **SPSR** & **SRNTT** & **Cycle-** & **CNN** & **VDSR** & **CrossNet** & **RRSGAN** & **STGAN** \\
**Dataset** & & & **[**13**]** & **[**14**]** & **[**17**]** & **[**61**]** & **[**7**]** & **[**61**]** & **[**7**]** & **[**16**]** & **[**43**]** & **[**Ours**]** \\ \hline \multirow{8}{*}{1 st test set} & LPIPS\(\downarrow\) & 0.4312 & 0.1871 & 0.1804 & 0.1807 & 0.2566 & 0.2629 & 0.2411 & 0.1060 & 0.1059 \\  & PL\(\downarrow\) & 7.1055 & 4.6872 & 3.4029 & 5.4150 & 6.2589 & 6.3553 & 6.1942 & 3.4206 & 4.3911 \\  & PSNR\(\uparrow\) & 29.5477 & 26.3259 & 27.1051 & 30.4578 & 31.3887 & 31.4109 & 31.3977 & 30.8390 & 31.4151 \\  & SSIM\(\uparrow\) & 0.7916 & 0.7797 & 0.7442 & 0.8062 & 0.8402 & 0.8398 & 0.8404 & 0.8123 & 0.8408 \\  & RMSE\(\downarrow\) & 11.9812 & 5.9048 & 5.7494 & 5.9353 & 5.8288 & 5.9756 & 5.7185 & 5.9496 & 5.5169 \\  & SAM\(\downarrow\) & 0.0212 & 0.0209 & 0.0145 & 0.0151 & 0.0129 & 0.0132 & 0.0128 & 0.0121 & 0.0116 \\ \hline \multirow{8}{*}{2 nd test set} & LPIPS\(\downarrow\) & 0.4278 & 0.2245 & 0.2135 & 0.2045 & 0.2845 & 0.2867 & 0.2754 & 0.1382 & 0.1085 \\  & PL\(\downarrow\) & 7.0390 & 4.7230 & 3.2028 & 5.3888 & 6.2557 & 6.2979 & 6.2375 & 3.4362 & 3.3668 \\  & PSNR\(\uparrow\) & 29.5116 & 27.2989 & 27.1212 & 30.2077 & 30.9905 & 30.8611 & 31.0788 & 30.1294 & 30.3556 \\  & SSIM\(\uparrow\) & 0.7644 & 0.7557 & 0.7072 & 0.7853 & 0.7852 & 0.7707 & 0.7783 & 0.7824 & 0.7990 \\  & RMSE\(\downarrow\) & 11.8951 & 7.0831 & 6.8115 & 6.7203 & 6.4642 & 6.6314 & 6.5236 & 7.7645 & 6.3602 \\  & SAM\(\downarrow\) & 0.0210 & 0.0251 & 0.0172 & 0.0171 & 0.0143 & 0.0146 & 0.0146 & 0.0141 & 0.0104 \\ \hline \multirow{8}{*}{3 rd test set} & LPIPS\(\downarrow\) & 0.5279 & 0.2569 & 0.2263 & 0.2562 & 0.3658 & 0.3742 & 0.3188 & 0.1632 & 0.1570 \\  & PL\(\downarrow\) & 7.0488 & 4.3254 & 3.2085 & 5.5561 & 6.5677 & 6.5961 & 6.4299 & 3.1879 & 4.0744 \\  & PSNR\(\uparrow\) & 27.7915 & 25.6126 & 25.7830 & 28.5190 & 29.3186 & 29.1658 & 29.5466 & 28.4583 & 28.7841 \\  & SSIM\(\uparrow\) & 0.7283 & 0.7121 & 0.6811 & 0.7481 & 0.7624 & 0.7568 & 0.7611 & 0.7471 & 0.7707 \\  & RMSE\(\downarrow\) & 14.6464 & 8.1976 & 9.2189 & 8.4218 & 8.3163 & 8.5115 & 8.5625 & 9.1680 & 8.1407 \\  & SAM\(\downarrow\) & 0.0260 & 0.0287 & 0.0182 & 0.0214 & 0.0184 & 0.0189 & 0.0169 & 0.0166 & 0.0163 \\ \hline \multirow{8}{*}{4 th test set} & LPIPS\(\downarrow\) & 0.4083 & 0.2357 & 0.2342 & 0.2172 & 0.2978 & 0.2990 & 0.2918 & 0.1632 & 0.1211 \\  & PL\(\downarrow\) & 7.1417 & 5.0191 & 3.5213 & 5.8543 & 6.6818 & 6.6931 & 6.6999 & 3.6107 & 4.9014 \\ \cline{1-1}  & PSNR\(\uparrow\) & 30.0461 & 27.6867 & 26.9556 & 30.5910 & 31.2929 & 31.2391 & 31.2405 & 30.1921 & 30.2993 \\ \cline{1-1}  & SSIM\(\uparrow\) & 0.7854 & 0.7662 & 0.7904 & 0.7732 & 0.7986 & 0.7971 & 0.7983 & 0.7684 & 0.7902 \\ \cline{1-1}  & RMSE\(\downarrow\) & 11.3478 & 7.4424 & 7.4681 & 7.1421 & 6.7719 & 6.9436 & 6.9173 & 9.1706 & 3.7800 \\ \cline{1-1}  & SAM\(\downarrow\) & 0.0201 & 0.0264 & 0.0188 & 0.0181 & 0.0150 & 0.0154 & 0.0155 & 0.0166 & 0.0066 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of the different methods. \(\downarrow\) indicates that the lower the value of the indicator the better and \(\uparrow\) indicates that the higher the value of the indicator the better. The results of the visual comparison are shown in Figure 8, providing a visual complement to the quantitative assessment. It is observed that the traditional bicubic interpolation, although capable of enlarging the image, is unable to create additional details, resulting in limited quality improvement. Conversely, deep-learning-based methods, especially CNN-based SISR techniques such as SPSR, VDSR, and ESRGAN, although capable of reconstructing texture details to a certain extent, often face the problem of blurring outlines.