# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 5 Conclusions  
In this paper, we explore the use of reference (Ref) images to reconstruct low-resolution (LR) images for remote sensing missions. We innovatively design STGAN, a hypersegmented reconstruction model for remote sensing images that fuses Generative Adversarial Networks (GANs) with a self-attention mechanism. The model provides an end-to-end network architecture that integrates a feature extraction module for residual structure and a dual-channel feature alignment and transfer mechanism based on CNN and Swin Transformer. This mechanism is able to accurately extract and align the features of Ref images, which effectively enhances the texture details of LR images and achieves high-quality super-resolution reconstruction of remote sensing images. Through extensive experimental validation, the STGAN model demonstrates good effectiveness and robustness on several standard datasets, proving the significant potential of the STGAN model in super-resolution reconstruction of remote sensing images. In the next step, we will investigate the performance of the RefSR method in improving the image quality with the existing scaling factors, and explore its efficiency and effectiveness in dealing with larger scaling factors. In addition, we will work on optimizing the computational efficiency of the model to improve utility and scalability while maintaining high-quality output.  
Conceptualization, W.H. and X.Z.; methodology, W.H. and X.Z.; software, W.H. and Y.Z.; validation, X.Z. and S.Y.; formal analysis, W.H.; investigation, W.H. and X.Z.; resources, X.Z., S.Y. and Y.Z.; writing--original draft preparation, W.H. and X.Z.; writing--review and editing, W.H., Q.Z. and N.H.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Qinghai Province Applied Basic Research Program project (2024-ZJ-716).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Dataset** & **Metric** & \begin{tabular}{c} \(\alpha/\beta/\gamma/\delta\) = \\ **1/0.005/1/0.005** \\ \end{tabular} & \
\begin{tabular}{c} \(\alpha/\beta/\gamma/\delta\) = \\ **0.1/0.001/1/0.001** \\ \end{tabular} &
\begin{tabular}{c} \(\alpha/\beta/\gamma/\delta\) = \\ **0.01/0.001/1/0.001** \\ \end{tabular} \\ \hline \multirow{3}{*}{1 st test set} & LPIPS\(\downarrow\) & 0.1098 & 0.1059 & 0.1218 \\  & P\(\downarrow\) & 4.6800 & 4.3911 & 4.3600 \\  & PSNR\(\uparrow\) & 31.2500 & 31.4151 & 31.4700 \\  & SSIM\(\uparrow\) & 0.8360 & 0.8408 & 0.8400 \\ \hline \multirow{3}{*}{2 nd test set} & LPIPS\(\downarrow\) & 0.1092 & 0.1085 & 0.1190 \\  & P\(\downarrow\) & 3.4700 & 3.3668 & 3.2800 \\  & PSNR\(\uparrow\) & 30.1600 & 30.3556 & 30.4300 \\  & SSIM\(\uparrow\) & 0.7950 & 0.7990 & 0.7987 \\ \hline \multirow{3}{*}{3 rd test set} & LPIPS\(\downarrow\) & 0.1580 & 0.1570 & 0.1750 \\  & P\(\downarrow\) & 4.1100 & 4.0744 & 4.2800 \\  & PSNR\(\uparrow\) & 28.6100 & 28.7841 & 28.9900 \\  & SSIM\(\uparrow\) & 0.7630 & 0.7707 & 0.7703 \\ \hline \multirow{3}{*}{4 th test set} & LPIPS\(\downarrow\) & 0.1240 & 0.1211 & 0.1350 \\  & P\(\downarrow\) & 5.1900 & 4.9014 & 5.0900 \\ \cline{1-1}  & PSNR\(\uparrow\) & 30.3000 & 30.2993 & 30.5400 \\ \cline{1-1}  & SSIM\(\uparrow\) & 0.7890 & 0.7902 & 0.7900 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results for different \(\alpha/\beta/\gamma/\delta\). In each row, the best results are highlighted in dark blue font.  
Data Availability Statement: The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors are grateful for the support of the Qinghai Provincial Laboratory for Intelligent Computing and Application platform.  
The authors declare no conflicts of interest.