Frequency-Aware Enhancement Network for Satellite Video Super-Resolution

Abstract

The lower quality of frames in satellite videos compared to natural videos poses significant challenges in capturing detailed information for alignment and fusion in the image space. In this paper, we introduce a novel frequency-aware enhancement network (FAENet) for satellite video super-resolution (SVSR), which tackles these challenges from a frequency-domain perspective. By leveraging frequency components, FAENet amplifies the distinctions between frames and between objects, thereby improving alignment and reconstruction accuracy. Firstly, the proposed Frequency Alignment Compensation Mechanism (FACM) incorporates a frequency-domain distribution alignment function to enable effective alignment compensation. This mechanism can be seamlessly integrated into existing alignment methods designed for natural video, thereby enhancing their applicability to SVSR tasks. Secondly, we introduce the Frequency Prompt Enhancement Block (FPEB), which facilitates edge reconstruction by leveraging frequency-domain prompts to distinguish objects from artifacts, thereby improving the clarity and accuracy of reconstructed edges. The proposed FAENet achieves 35.33 dB PSNR on the Jilin-189 dataset and 40.57 dB on the SAT-MTB-VSR dataset, outperforming other state-of-the-art compared methods and demonstrating its effectiveness and robustness in addressing the unique challenges of SVSR.