Satellite Video Super-Resolution With Implicit Interpolation-Based Alignment and Spatiotemporal Fusion

Satellite Video Super-Resolution (SVSR) is a critical task in remote sensing, aiming to reconstruct high-resolution videos from low-resolution satellite observations. Compared to natural-scene video super-resolution, SVSR faces unique challenges, such as weak texture regions, large-scale inter-frame displacements, and complex motion patterns, which limit the performance of existing methods in terms of alignment accuracy, detail restoration, and computational efficiency. To address these issues, we propose ISSVSR, a lightweight SVSR network tailored for remote sensing scenarios. First, we introduce an implicit interpolation mechanism into the classical flow-guided alignment framework and design the Implicit Interpolation-based Alignment Module (IIAM) to replace traditional warp methods based on explicit interpolation. This enhancement improves alignment accuracy and preserves more high-frequency details under complex motion conditions. Second, we design the Spatiotemporal Fusion Module (SFM), which combines temporal and spatial attention mechanisms for more effective multi-frame feature aggregation, thereby improving reconstruction quality. Finally, by combining lightweight architecture and module-level optimizations, we significantly reduce the computational complexity of the model. Extensive experiments demonstrate that ISSVSR achieves a better balance between reconstruction accuracy, perceptual quality, and computational efficiency, verifying its effectiveness in satellite video super-resolution tasks. The source code will be available at:https://github.com/DylanFantast/ISSVSR