DR-Diff: Dual Residual-Guided Diffusion Model for Infrared Image Super-Resolution

Infrared image super-resolution is essential in optical imaging systems to enhance detail recovery for applications such as thermal analysis and night vision. However, it remains challenging due to inherent limitations like low resolution, high noise, and weak contrast. While diffusion-based methods have shown promise, most rely only on low-resolution conditioning, which offers limited high-frequency constraints and lacks adaptive refinement during sampling. To address this, we propose DR-Diff, a spatiotemporal-aware dual residual-guided diffusion model. Our framework integrates a static global residual prior and a dynamic local residual feedback, forming a closed-loop guidance mechanism that supports persistent structure preservation and stepwise local adaptation. Furthermore, a spatiotemporal fusion module dynamically balances conditioning signals via temporal encoding and spatial self-attention, enabling coarse-to-fine reconstruction. Experiments on benchmarks confirm that our approach outperforms existing state-of-the-art methods. © 2026 IEEE.