# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
## 5 Conclusions  
In this paper, we introduce a lightweight and efficient image SR reconstruction network RSTSRN based on the Swin Transformer module using the Laplace image pyramid as a framework. The efficiency of the proposed network is mainly due to the following reasons: (1) We use the Laplace image pyramid as the network framework, which breaks the hard 8\(\times\) SR task into three simple 2\(\times\) SR tasks so that 8\(\times\) SR can be performed with a lightweight network RSTSRN. (2) We adopt the parameters-sharing mechanism between the pyramid levels and between the recursive blocks to maintain a low-parameter quantity. (3) We replace the convolution layers with more efficient Swin Transformer modules and adopt the skip connection mechanism to enable the network to achieve higher performance while maintaining low computational complexity.  
Despite the positive outcomes of this study, there are some limitations. For example, in terms of the number of parameters, it is still relatively large compared to the latest methods such as SPIN, DiVANet, NGswin, etc., and further lightweight design is needed. To address the limitations of the current work, future research can be conducted in the following directions: one is to further optimize the network structure to further reduce the number of parameters; another is to train and test on more diverse datasets, such as images obtained by ExoMars Trace Gas Orbiter (TGO)'s Color and Stereo Surface Imaging System (CaSSIS) and High-Resolution Imaging Camera (HiRIC) on China's First Mars Exploration Tianwen-1 Mission to enhance the model's generalization ability. Through these research approaches, we hope to elevate the super-resolution reconstruction technology for Mars images to a higher level.  
Conceptualization, F.W. and X.J.; methodology, F.W.; software, F.W. and T.F.; validation, F.W., T.F. and Y.F.; formal analysis, F.W., T.F. and D.X.; investigation, F.W., Y.F. and C.Z.; writing--original draft preparation, F.W. and D.X.; writing--review and editing, F.W. and C.Z.; visualization, F.W.; project administration, F.W.; funding acquisition, F.W. and X.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Jilin Province, grant number 20220101168 JC, as well as funded by the National Natural Science Foundation of China, grant number 42001345, and funded by the Key Laboratory of Lunar and Deep Space Exploration, Chinese Academy of Sciences, grant number LDSE201901.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
AWSRN Adaptive Weighted Super-Resolution Network  
CARN CAScading Residual Network  
CNN Convolutional Neural Network  
DAT Dual Aggregation Transformer  
DEGREE Deep Edge Guided Recurrent rEsidual  
DIV2K DIVense 2K Resolution image dataset  
DiVANet Directional Variance Attention Network  
DRCN Deeply Recursive Convolutional Network  
DRRN Deep Recursive Residual Network  
D-DBPN Dense Deep Back-Projection Networks