Learning a Local-Global Alignment Network for Satellite Video Super-Resolution

Satellite video is a novel data source for earth observation, which can be applied in multiple fields for dynamic monitoring. It is always equipped with high temporal resolution at the cost of low spatial resolution of tiny moving objects. Video super-resolution (VSR) is utilized to improve the spatial resolution of satellite video and obtain high spatial-temporal resolution data. However, most existing VSR methods mainly focus on the local interframe information during feature alignment, which lack the ability to model long-distance correspondence. In this letter, a novel two-branch alignment network with an efficient fusion module is proposed for satellite VSR. Both deformable convolution (DCN) and transformer-like attention are employed to fully explore the local and global information between frames. Furthermore, a fusion module is proposed to model the residuals between fusion features and compensate them for better fusion. Experiments on Jilin-1 satellite videos demonstrate that the proposed network can achieve comparable results to current state-of-the-art (SOTA) VSR methods with tiny parameters.