# RSTSRN: Recursive Swin Transformer Super-Resolution Network for Mars Images
## 1 Introduction  
Exploration is the driving force for the development of human civilization and social progress. In the course of human exploration, space exploration can most directly expand the territory of human cognition and is extremely challenging. If we can understand the history of Mars, mankind's view of the entire solar system may change. By studying the morphological characteristics of the Martian surface, we can gain a deep understanding of the formation and evolution process of the Martian surface and the evolutionary history of Martian geology [1; 2; 3; 4]. Therefore, the primary task of Mars exploration is to obtain global high-resolution optical images. The earliest global images of Mars were obtained in the early 1970s with a resolution of only 1 km. The Viking 1 and Viking 2 orbiters in the late 1970s obtained global images of Mars with a resolution of 100\(\sim\)200 m. The Mars Global Surveyor (MGS) and Mars Express have obtained global images of Mars with a resolution of several meters. It took humans 30 years to increase the resolution of images of Mars by hundreds of times. The High-Resolution Imaging Science Experiment (HiRISE) [5; 6; 7; 8; 9] of the Mars Reconnaissance Orbiter (MRO) launched in 2005 has further increased the resolution by 10 times, and it obtained Mars remote sensing images with a resolution of up to 0.3 m. HiRISE's excellent resolution has created many new research topics for planetary geology researchers: how the stratigraphic layers in the interior of Mars are formed; what are the new impact craters on thesurface of Mars; what are the dynamic changes on the surface of Mars (such as how sand dunes move). The high-resolution images provided by HiRISE can help researchers explore Martian plates, volcanoes, climate and seasonal effects as well as understand the mysteries of impact crater formation, magma alluvial and glaciation on Mars. Various physical processes have left rich and colorful patterns on the surface of Mars. High-resolution imaging data can help researchers explore the mysteries of Martian plates, volcanoes, climate, and seasonal effects as well as the formation of impact craters, magma accretion, and glaciation on Mars. However, too high resolution also comes at a cost. On the one hand, the volume of obtained data is too large, and it takes a long time to send it back to the earth due to the limitation of data downlink bandwidth. On the other hand, the field of view is limited, and the images obtained so far only cover about a few percent of the Martian surface. In order to maximize scientific output, it is necessary to further increase the resolution of acquired images, so image super-resolution (SR) reconstruction techniques have become the best choice. At the same time, the SR will also provide planetary geology researchers with finer and more microscopic image data information so as to open up new research topics and further understand the formation and evolution process of the Martian surface and the evolution history of Martian geology. The types of surface features on Mars are monotonous with no vegetation or other features except for bare soil and gravel. Compared with natural images, Mars images have more monotonous texture details and equally monotonous color information. This is extremely challenging for image super-resolution reconstruction, because the monotonous image information is difficult for feature learning mechanisms to obtain the high-frequency information required for super-resolution reconstruction. In this work, we propose a novel Recursive Swin Transformer Super-Resolution Network (RSTSRN) for image SR. The RSTSRN improves upon the LapSRN (Laplacian pyramid Super-Resolution Network) [10] in three aspects: (1) it uses Residual Swin Transformer Block (RSTB) for more effective residual learning; (2) the idea of parameter sharing was introduced to reduce the number of parameters; (3) a multi-scale training strategy was designed to accelerate convergence speed.