RADiffSR: A Diffusion Model for Remote Sensing Image Super-Resolution Fusing Residual Attention and Cross-Scale Dynamic Gating

Remote sensing image super-resolution (SR) technology is critical for enhancing the fine interpretation capability of large-scale land cover elements. However, existing methods are constrained by three core deficiencies: insufficient information interaction in multispectral channel modeling, lack of spatiotemporal continuity modeling for geographic entities, and failure of cross-scale feature geometric alignment. These deficiencies lead to coupled challenges in reconstructed images, including morphological discontinuities of extensive geographic features and texture artifact proliferation. This paper proposes a remote sensing image SR algorithm based on the diffusion probabilistic model (DPM), referred to as RADiffSR. First, a Residual-Attention Enhancement Block (RAE Block) is designed. It integrates residuals and Nonlinear Activation-Free Block to form a dual-domain attention mechanism, which synchronously optimizes feature response weights in the spatial and spectral domains, alleviating the deficiency in correlation representation between multispectral channels. Second, we introduce large-kernel convolutional layers to construct multi-level receptive field architectures aligned with geographic entity scale characteristics, modeling extensive terrain continuity through enlarged kernel sizes while incorporating inverted bottleneck ConvFFN structures to deepen feature extraction and implicitly enhance high-frequency texture retention. Finally, a feature manifold alignment strategy is implemented with dynamic gating mechanisms between encoder-decoder pathways to regulate cross-scale feature propagation weights, suppressing semantic distortion and high-frequency information loss. We construct the GF7-SR super-resolution dataset based on GF-7 satellite imagery, encompassing diverse typical land cover scenarios including mountainous houses, farmland, forests, and water bodies for model training and testing. Experiments demonstrate that RADiffSR achieves 36.39 dB and 28.36 dB PSNR on GF7-SR and Toronto datasets respectively, significantly outperforming state-of-the-art methods.