# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction  
[PERSON]  
1 School of Computer Technology and Applications, Qinghai University, Xining 810016, China; [EMAIL_ADDRESS] (W.H. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (N.H.) 1  
[PERSON]  
1 School of Computer Technology and Applications, Qinghai University, Xining 810016, China; [EMAIL_ADDRESS] (W.H. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (N.H.) 1  
[PERSON]  
1 School of Computer Technology and Applications, Qinghai University, Xining 810016, China; [EMAIL_ADDRESS] (W.H. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (N.H.) 1  
[PERSON]  
1 School of Computer Technology and Applications, Qinghai University, Xining 810016, China; [EMAIL_ADDRESS] (W.H. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.Z. ); [EMAIL_ADDRESS] (N.H.) 1  
[PERSON]  
1 School of Computer Technology and Applications, Qinghai University, Xining 810016, China; [EMAIL_ADDRESS] (W.H. ); jie  
techniques are mainly based on interpolation and reconstruction methods. In recent years, the rise of deep learning has revolutionized image SR reconstruction [6], especially the application of convolutional neural networks (CNNs) [7; 8; 9], which makes it realistic to establish the mapping relationship between LR and HR by analyzing and learning from a large amount of image data [10]. Image SR reconstruction techniques can be categorized into single image super-resolution (SISR) and multi-image super-resolution (MISR) based on the number of images involved in the process [11]. The goal of the SISR technique is to reconstruct the HR image from a single LR image, but due to the loss of high-frequency details in the acquisition process, this technique can lead to blurred and unsharp results when recovering details, especially at high magnifications. To overcome the challenges faced by SISR, Generative Adversarial Network (GAN)-based SISR methods have emerged [12; 13; 14], which have made significant progress in improving visual quality. GANs are capable of generating more realistic textures and details through an adversarial training process, and thus, solving the blurring and detail loss problems in traditional SISR methods to some extent. However, GAN-based SISR methods also have drawbacks and may sometimes generate unrealistic textures or even introduce artefacts. With the development of SISR technology, the reference-based super-resolution (RefSR) technique has begun to receive attention [15; 16; 17; 18]. RefSR utilizes an HR reference image similar to the LR image to enhance the details, which compensates for the shortcomings of SISR, and especially excels in processing similar content. However, natural variations, cloud occlusion, and difficulties in acquiring high-resolution reference images challenge the robustness of the model. In order to enhance the usefulness of super-resolution in remote sensing images, we developed a novel RefSR model, STGAN, which is capable of outputting high-quality images stably in various environments. Contribution:  
1. We have innovatively designed a remote sensing image RefSR method (STGAN) based on the Generative Adversarial Networks (GANs) and self attention mechanism. STGAN adopts the GAN structure and introduces a specific improved Swin Transformer model. At the same time, it combines the residual network, the self-attention mechanism, and the dual-channel feature extraction of CNN to achieve accurate pixel-level fusion and significantly improve the quality of image reconstruction. 2. The experimental results verify the excellent performance of STGAN in the field of super-resolution, which not only excels in many SISR solutions, but also demonstrates obvious advantages in comparison with mainstream RefSR methods.