Blind Super-Resolution based on Inter-Frame Information Compensation for Satellite Video

Super-Resolution (SR) of satellite video has long been a critical research direction in the field of remote sensing video processing and analysis, and blind SR has attracted increasing attention in the face of satellite video with unknown degradation. However, existing blind SR methods mainly focus on accurate blur kernel estimation, while ignoring the importance of inter-frame information compensation in the time domain. Therefore, this paper focuses on precise temporal information compensation and proposes a blind SR Network based on Inter-Frame Information Compensation (IFIC-SRNet). First, we propose a Multi-Scale Parallel Convolution block (MSPC) to alleviate the difficulty of alignment between satellite video frames due to the presence of moving objects of different scales. Second, we propose a Hybrid Attention-based Feature Extraction Module (HAFEM) that effectively extracts both local and global information between video frames. While activating more pixels, more attention is allocated to informative pixels to obtain the clean features. Finally, a Pyramid Space Activation Module (PSAM) is proposed to gradually adjust the clean features through a multi-layer iterative pyramid structure, enabling the clean features to better perceive blur and achieve pixel-level fine compensation for unknown degraded frames. Extensive experiments on real satellite video datasets demonstrate that our method is superior to state-of-the-art non-blind and blind SR methods, both qualitatively and quantitatively.