# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 4 Experiments
### Ablation Experiments
* 4 Hyperparameter Tuning of Loss Weight: In our experiments, we used the same training strategy and network parameters, only adjusting the values of \(\alpha/\beta/\gamma/\delta\). As shown in Table 6, the model using the parameter combination \(\alpha/\beta/\gamma/\delta=0.1/0.001/1/0.001\) performs well on several key metrics. On PSNR and SSIM metrics, the parameter combination achieves the best results on different test sets several times, indicating that the model is able to effectively improve the quality of image reconstruction and maintain structural consistency. Meanwhile, the excellent performance on the LPIPS metric further validates the model's advantage in perceptual quality. These results indicate that the parameter combination has good \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Dataset** & **Metric** & \
\begin{tabular}{c} **STGAN (with** \\ **RAM-V**) \\ \end{tabular} &
\begin{tabular}{c} **STGAN (Without** \\ **RAM-V**) \\ \end{tabular} \\ \hline
1 st test set & PSNR\(\uparrow\) & 31.4151 & 30.2721 \\  & SSIM\(\uparrow\) & 0.8408 & 0.8262 \\ \hline
2 nd test set & PSNR\(\uparrow\) & 30.3556 & 29.2645 \\  & SSIM\(\uparrow\) & 0.7990 & 0.7700 \\ \hline
3 rd test set & PSNR\(\uparrow\) & 28.7841 & 27.7111 \\  & SSIM\(\uparrow\) & 0.7707 & 0.7424 \\ \hline
4 th test set & PSNR\(\uparrow\) & 30.2993 & 29.2344 \\  & SSIM\(\uparrow\) & 0.7902 & 0.7628 \\ \hline \hline \end{tabular}  
* 3 Effectiveness of gradient loss: We analyze the impact of gradient loss. “Baseline” refers to the use of only common SR loss functions, including reconstruction loss Lrec, adversarial loss Ladv, and perceptual loss Lper. We added gradient-based reconstruction loss Lgrec and gradient-based adversarial loss Lgadv in turn. As shown in Table 5, the gradient loss significantly improves the PI and LPIPS compared to the baseline model, suggesting that it produces more realistic visuals.  
\end{table} 
Table 4: The effect of the RAM-V ablation experiment, which contains RAM-V, is the complete design of the STGAN in this paper. \begin{table} \begin{tabular}{c c c c c} \hline \hline
**Test Dataset** & **Metric** & **Baseline** & **STGAN (with \(\mathcal{L}_{g\_\text{rec}}\))** &
\begin{tabular}{c} **STGAN (with \(\mathcal{L}_{g\_\text{rec}}\)** \\ **and \(\mathcal{L}_{g\_\text{adv}}\))** \\ \end{tabular} \\ \hline
1 st test set & \begin{tabular}{c} LPIPS\(\downarrow\) \\ \(\text{PI}\)\(\downarrow\) \\ \(\text{PSNR}\)\(\uparrow\) \\ \(\text{SSIM}\)\(\uparrow\) \\ \end{tabular} & \begin{tabular}{c} 0.1589 \\ 3.6247 \\ 29.5883 \\ \(\text{0.7678}\) \\ \end{tabular} & \
\begin{tabular}{c} 0.1034 \\ 3.3038 \\ 29.5490 \\ \(\text{0.7839}\) \\ \end{tabular} &
\begin{tabular}{c} 0.1059 \\ 4.3911 \\ 31.4151 \\ 0.8408 \\ \end{tabular} \\ \hline
2 nd test set & \begin{tabular}{c} LPIPS\(\downarrow\) \\ \(\text{PI}\)\(\downarrow\) \\ \(\text{PSNR}\)\(\uparrow\) \\ \end{tabular} & \begin{tabular}{c} 0.1986 \\ 3.5580 \\ 29.1053 \\ \end{tabular} & \
\begin{tabular}{c} 0.1367 \\ 3.4702 \\ 28.9768 \\ \end{tabular} &
\begin{tabular}{c} 0.1085 \\ 3.3668 \\ 30.3556 \\ 0.7990 \\ \end{tabular} \\ \hline
3 rd test set & \begin{tabular}{c} LPIPS\(\downarrow\) \\ \(\text{PI}\)\(\downarrow\) \\ \(\text{PSNR}\)\(\uparrow\) \\ \end{tabular} & \begin{tabular}{c} 0.2140 \\ 3.2653 \\ 27.5391 \\ 0.7010 \\ \end{tabular} & \
\begin{tabular}{c} 0.1592 \\ 3.3378 \\ 27.6726 \\ 0.7298 \\ \end{tabular} &
\begin{tabular}{c} 0.1570 \\ 4.0744 \\ 28.7841 \\ 0.7707 \\ \end{tabular} \\ \hline
4 th test set & \begin{tabular}{c} LPIPS\(\downarrow\) \\ \(\text{PI}\)\(\downarrow\) \\ \(\text{PSNR}\)\(\uparrow\) \\ \end{tabular} & \
\begin{tabular}{c} 0.2175 \\ 3.6153 \\ 29.1843 \\ 0.7163 \\ \end{tabular} &
\begin{tabular}{c} 0.1589 \\ 3.7088 \\ 29.0762 \\ 0.7381 \\ \end{tabular} \\ \hline \hline \end{tabular} 
* 4 th test set & \begin{tabular}{c} LPIPS\(\downarrow\) \\ \(\text{PI}\)\(\downarrow\) \\ \(\text{PSNR}\)\(\uparrow\) \\ \(\text{SSIM}\)\(\uparrow\) \\ \end{tabular}  & \begin{tabular}{c} 0.2175 \\ 3.6153 \\ 29.1843 \\ 0.7163 \\ \end{tabular}  & \