EGDM-IRSR: Edge-Guided Diffusion Model with State-Space UNet for Super-Resolution Infrared Images

Ensuring infrared images are of super-high resolution is crucial for enhancing thermal imaging systems’ visual perception, yet existing methods struggle to recover sharp edges and textual details. Therefore, in this study, we aimed to address the following issues: over-smoothed edges, distorted radiometric contrast in diffusion-based approaches, and scanning artifacts introduced by efficient state-space models like Mamba. We propose a novel edge-guided diffusion framework named EGDM-IRSR. Its core methodology integrates a multi-modal scanning mechanism employing complementary scan paths with content-aware modulation to mitigate directional artifacts, along with an edge guidance branch with learnable direction-aware convolutions, complemented by edge-frequency composite loss. Extensive experiments conducted on public benchmarks demonstrate that our method significantly outperforms state-of-the-art alternatives in quantitative metrics and exhibits superior visual fidelity by effectively preserving edges and fine structures. Ablation studies validate the effectiveness of each proposed component. We conclude that EGDM-IRSR provides a more robust and detail-enriched solution for acquiring super-resolution infrared images by synergistically integrating edge guidance with enhanced sequential modeling.