DFCFNet: A Local–Nonlocal Dual-Branch Feature Complementary Fusion Network for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (RSISR) has gained significant attention in recent years due to its critical role in enhancing image analysis capabilities. While existing methods often focus on nonlocal feature extraction, they frequently overlook the importance of local information integration. Moreover, many methods reconstruct images by introducing more complex structures, which poses a challenge to resource-limited devices. To address these issues, we present a local–nonlocal dual-branch feature complementary fusion network (DFCFNet) featuring two key components: a lightweight dual-branch feature aggregation (DBFA) module and an Efficient Feed-Forward Network (EFFN). The DBFA employs a dual-branch structure comprising a Focused Local Feature Branch (FLFB) with novel Partial Convolution Channel Mixers for localized pattern modeling and a Non-Focal Exploration Branch (NFEB) utilizing global variance analysis for comprehensive feature extraction. This dual-branch design enables simultaneous capture of local and global contextual information. The EFFN is designed to further refine the features of the DBFA output in order to make full use of the detailed information of the image. Extensive experimental results show that the proposed DFCFNet reconstructs optimally on remote sensing datasets and is also optimal in terms of computational efficiency and network complexity. The framework’s versatility is further confirmed through successful adaptation to natural image SR tasks, showing consistent performance improvements across five standard datasets.