Dual-Branch Super-Resolution (DBSR): Lightweight DBSR Network for Enhancing Remote Sensing Images

Recent advances in deep learning-based super-resolution (SR) techniques for remote sensing images (RSIs) have shown significant promise. However, these performance improvements often come at a high computational cost, which limits their practical application. To address this issue, this paper proposes a dual-branch SR model (DBSR) that enhances both model performance and efficiency through primary and auxiliary branches. The primary branch integrates the advantages of channel recalibration, a separable swin transformer (SST), and a spatial refinement module to achieve fine-grained feature extraction. The SST serves as the core of the primary branch, employing hierarchical window attention calculations to facilitate lightweight and effective multiscale feature representation. Conversely, the auxiliary branch enhances shallow features through a global information enhancement module, which mitigates the misleading effects of directly upsampling these shallow features on the SR results. Comparative and ablation experiments conducted on four RSI datasets and five SR benchmark datasets demonstrate that our DBSR method effectively balances the number of parameters with performance, showcasing its potential for application in RSI processing.