KAN-Enhanced Alignment and Fusion for Lightweight Satellite Video Super-Resolution

Satellite video super-resolution (SVSR) aims to reconstruct high-resolution video frames from low-resolution satellite observations, providing enhanced visual details for remote sensing applications. Despite recent progress, existing methods still suffer from limited alignment accuracy under complex motion and insufficient feature aggregation across frames, which restricts reconstruction quality. To address these issues, we propose a lightweight SVSR framework that incorporates Kolmogorov–Arnold Networks (KAN) into both the alignment and fusion processes. Specifically, a KAN-based spatial attention module is introduced to enhance the first-order and second-order neighboring frames, improving the accuracy of frame alignment. In addition, a KAN-based channel attention mechanism is adopted to facilitate more effective multi-frame feature aggregation. Benefiting from these designs, the proposed framework achieves strong reconstruction capability while maintaining a lightweight model structure. Extensive experiments demonstrate that the proposed method achieves superior performance in terms of PSNR, SSIM, LPIPS, and tOF compared with existing approaches, verifying the effectiveness of integrating KAN into SVSR.