# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 3 Method
### Network Architecture
#### 3.2.1 Generator
The module keeps the (S)W-MSA mechanism of Swin Transformer unchanged while adding a convolutional layer at the end of it to further enhance the local feature extraction capability. In addition, DST fixes the size of the feature maps at each stage to improve the stability and consistency of feature extraction while maintaining the global context modeling capability. In this design, PCM and DST extract local and global features independently. To enhance the representational capability of the model, LFRC performs channel-level fusion of the features extracted by PCM and DST to generate a rich feature representation that integrates both global and local information. In the RefSR method, the module is designed to process multiple input data streams, including low-resolution (LR) images, reference (Ref) images, and their corresponding gradient maps, separately. Processing a set of corresponding images and gradient maps at a time allows the module to simultaneously analyze and integrate information from different sources, thus significantly improving the quality of the super-resolution reconstruction. By fusing local and global information, the module not only enhances the overall performance of the image, but also uses the gradient information to further optimize the edge and texture details, resulting in more accurate feature alignment and generating more natural and realistic visual effects. 3. High-quality remote sensing image reconstruction module Instead of simply merging various image features as well as gradient feature map features, we employ a fine-grained feature fusion strategy. Our goal is to enhance the correlation between the two features while suppressing the less correlated information to optimize the texture transfer process. As shown in Figure 5, the first and second set of features are first fused at the channel layer, and then attention maps are generated through a series of convolutional layers. These attention maps are normalized by a sigmoid function and elementwise multiplied with the second set of features to strengthen the correlation between them. Next, the attention maps are again convolved and the results are summed with the previous results to further enrich the features. Finally, the enriched features are fused with the first set of features at the channel level to provide more accurate and enriched information for the subsequent texture transfer process. The Robustness Augmentation Module (RAM-V) is employed, the equation of which can be expressed as:\[\mathrm{f_{RAM-V}}=\mathrm{H_{cnn-1}}(\mathrm{f_{first}}\oplus\mathrm{f_{second}}) \odot\mathrm{f_{second}}+\mathrm{H_{cnn-2}}(\mathrm{f_{first}}\oplus\mathrm{f_{ second}}) \tag{3}\]  
\[\mathrm{F_{RAM-V}}=\mathrm{f_{RAM-V}}\oplus\mathrm{f_{first}} \tag{4}\]  
where \(\mathrm{f_{RAM-V}}\) denotes the summed enriched features, \(\mathrm{f_{first}}\) and \(\mathrm{f_{second}}\) denote the first set of features and the second set of features, respectively, and \(\mathrm{H_{cnn-1}}(\cdot)\) and \(\mathrm{H_{cnn-2}}(\cdot)\) denote the computation including convolutional layers. In the high-quality image reconstruction module, the data are first processed by the RAM-V module and CNN, followed by residual concatenation with the low-resolution data. After multiple convolutions and one-pixel rearrangement, the final output is a high-resolution super-resolution image, thus completing the entire super-resolution reconstruction process. In this case, the pixel rearrangement operation is shown in Figure 6.  
Figure 4: Generator feature alignment and transfer module with dual channels, where the symbol \(\uparrow\) denotes a dual-triple upsampling process and \(\downarrow\) denotes a dual-triple downsampling process. X2 represents a cycle of 2 and X4 represents a cycle of 4. Figure 5: Structure of the RAM-V.