EEMSAGAN: edge-enhanced multi-scale attention generative adversarial network for remote sensing image super-resolution

Abstract Image super-resolution (SR) reconstruction is a technique that generates high-resolution (HR) images from low-resolution (LR) inputs through algorithmic processing. However, existing super-resolution methods often fall short in restoring complex textures and producing images with rich details and sharp edges. To address these limitations, this paper proposes an Edge-Enhanced Multi-Scale Attention Generative Adversarial Network (EEMSAGAN). Specifically, we develop a dual-path generator architecture: the standard path thoroughly extracts deep high-level features, while the auxiliary path preserves prior information and high-frequency details. A hybrid attention-based dual-path feature fusion module adaptively integrates the features from both paths, enabling efficient utilization and complementary information flow, thereby enhancing the model’s feature representation capability. A multi-scale feature extraction module is introduced in the standard path to boost the model’s ability to capture features at various scales. Furthermore, to enhance edge detail recovery, an edge loss function is proposed to constrain the edge domain of the SR image, which facilitates high-frequency detail compensation and helps preserve critical texture details. Extensive comparisons with several state-of-the-art methods on multiple remote sensing scenes demonstrate that our EEMSAGAN generates SR images with richer details and clearer textures, verifying the effectiveness of the proposed approach. The code of EEMSAGAN will be available at https://github.com/LjWang-2002/EEMSAGAN .