SFEFeNet: A Structure-Frequency Mutual-Guided Lightweight Network for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution plays an important role in object recognition, urban monitoring, and fine-grained remote sensing interpretation. This paper studies lightweight single-image remote sensing image super-resolution, in which only one LR observation is available and the model must recover reliable structural details under a limited computational budget. Existing lightweight methods reduce parameter counts and computational complexity, but their limited representation capacity often causes blurred boundaries, broken road structures, and missing high-frequency details in buildings, roads, and texture-rich regions. To address these issues, we propose SFEFeNet, a Structure-Frequency Mutual-Guided Lightweight Network for remote sensing image super-resolution. First, we design a Lightweight Structure-Frequency Block (LSFB) to jointly model local spatial features, structural responses, and frequency responses with low computational overhead. Second, we introduce a Structure-Frequency Mutual Guidance (SFMG) module, where edge responses guide high-frequency component selection, and the selected high-frequency responses further refine edge-aware attention. Finally, we propose a Structure-Frequency Fusion Gate (SFFG) to adaptively integrate lightweight features, local spatial features, frequency-enhanced features, and structure-refined features. Experiments on RSSCN7, DOTA, and WHU-RS19 datasets evaluate SFEFeNet in terms of reconstruction quality, visual performance, and model complexity. Additional analyses further examine structural preservation, complex synthetic degradation, real-image generalization, and statistical stability. Notably, SFEFeNet-Lite contains 0.539 M parameters and 17.07 G FLOPs for ×2, and 0.622 M parameters and 7.12 G FLOPs for ×4, enabling effective structure-frequency feature modeling with lightweight computational cost.