Bi-IISR: Binarized One-Step Diffusion for Infrared Image Super-Resolution

Diffusion-based infrared image super-resolution (IISR) has shown strong capability in recovering fine structures and improving perceptual quality under real-world degradations. However, its heavy computational cost and memory footprint severely limit practical deployment on resource-constrained devices. To address these issues, we propose Bi-IISR, a binarized one-step diffusion framework for robust and structurally faithful infrared super-resolution under stringent efficiency constraints. First, we introduce a Degradation-Responsive Conditioning (DRC) module on the low-resolution conditioning branch to perform degradation rectification and structural compensation, thereby suppressing distortion amplification induced by binarized processing. Second, we incorporate the Shift-Gain Regulation Mechanism (SGRM) into the binarization procedure to prevent sign collapse caused by biased channel distributions and to counteract energy attenuation, stabilizing inter-layer propagation. Finally, we adopt a Multi-level Attention Distillation (MAD) loss that aligns intermediate representations of the binarized student with full-precision teacher, which stabilizes optimization and improves structural fidelity. Extensive experiments demonstrate that Bi-IISR substantially enhances robustness and boundary preservation for binarized IISR under real degradations, while markedly reducing computation and memory consumption.