From Unpaired Priors to Paired Calibration: T2IR for RGB-to-Infrared Image Translation

RGB-to-infrared (IR) image translation is difficult because visible reflected light and thermal radiation follow fundamentally different physical processes. Paired RGB/IR data are also scarce and frequently only weakly aligned, so a single-paradigm approach often fails to jointly maintain structural correspondence and IR appearance diversity. To tackle these issues, we propose T2IR, a two-stage diffusion framework that moves from unpaired prior learning to paired structural calibration. The first stage exploits separate RGB and IR image pools to acquire a cross-modal infrared appearance prior. The second stage uses paired supervision to adjust geometric structure and infrared intensity. We additionally apply training-time regularization on thermal sources and texture statistics, which mitigates underheated thermal regions and visible-texture leakage from RGB images. To support this framework, we construct THIRAlign, a synchronized paired RGB/IR dataset obtained with a beam-splitter-assisted dual-modal acquisition system and calibration-based spatial alignment. Experiments on three datasets demonstrate that T2IR surpasses six state-of-the-art methods. The results verify that unpaired infrared prior learning and paired structural calibration are complementary, and that the proposed training-time regularization compensates for the weakness of global image reconstruction losses.