A Recurrent Refinement Network for Satellite Video Super-Resolution

Deep learning-based methods have shown superior performance in VSR tasks. However, satellite video frames are characterized by large width, low resolution, and lack of features. Consequently, the conventional VSR method is not suitable for satellite video. In this paper, a recurrent refinement network is proposed. Considering that the vast majority of remote sensing images belong to the static background, a single-image SR (SISR) method is first used to obtain high-resolution features for a specific target frame. To further complement the missing details, the network learns the complementary information enhanced by an Encoder-Decoder structure from adjacent frames to refine the results of SISR. To measure the contribution of different adjacent frames to the recovery of the target frame, a temporal attention mechanism is introduced in the final fusion stage. The experiment on the video data of Jilin-1 demonstrates the effectiveness of our method.