LEARNING AN INTRINSIC GRAPH NEURAL NETWORK FOR SARTELLITE VIDEO SUPER-RESOLUTION

Existing video super-resolution (VSR) methods usually merge the redundant temporal information along frames to achieve information enhancement, which naturally discards the spatial redundancy information. This paper proposes an intrinsic Graph Neural Network (GNN) framework for satellite VSR to fully explore the internal spatial prior while considering the temporal information in the video frame sequence. Firstly, a Multi-Scale Deformable convolution (MSD) is adopted to accurately model the spatial-temporal relationship between frames. Then, we search for k-nearest neighbors to construct the spatial graph and profoundly excavate the prior spatial information brought by patch recurrence. Finally, the spatial-temporal redundant information is integrated and complementary. Experiments on Jilin-1 satellite video demonstrate the effectiveness of our framework.