LVP-DiffIR: Distilling Large-Model Trajectories for Thermal Infrared Image Restoration

Infrared image enhancement faces a persistent trade-off: compact networks trained on synthetic pairs are efficient but overfit the simulator, while large generative models are accurate yet computationally expensive at inference.We propose Large-model Visual Prior Diffusion Image Restoration (LVP-DiffIR), a large-to-small framework that distills an offline large-model iterative enhancement trajectory into a compact five-step student.The student copies both the first restore, which removes the dominant degradations, and the later residual refinement of edges and contrast.To save large-model inference time during distillation, we adapt the student with Median-Ratio Adaptation (MRA) on unlabeled real infrared: an IQA memory bank and a per-source median-ratio backward gate, built on an EMA teacher–student pair. The adapted student further outperforms pure trajectory distillation.We further introduce InfraRS, to our knowledge the highest-resolution public remote sensing dataset for infrared image enhancement.Distilled from offline RAR trajectories and then adapted with MRA on unlabeled real infrared, LVP-DiffIR reaches state-of-the-art no-reference perceptual scores on held-out real images, including InfraRS, without paired real-world ground truth on those captures.Our code will be released soon.