Interactive All-in-One Image Restoration and Fusion

摘要

Supervised infrared-visible image fusion (IVIF) often overfits limited training distributions, creating a critical generalization gap under open-world degradations (rain, haze, low light, noise, blur). To address this issue, we propose AIR-Fusion, a parameter-efficient adaptation of a frozen, restoration-capable latent diffusion backbone for degraded IVIF without full fine-tuning, transferring restoration priors for robust fusion. A Cross-Modal Bridging Adapter (CMBA) aligns infrared cues and textual instructions with the frozen diffusion conditioning space and injects them into multi-scale denoising features to steer instruction-guided restoration-aware fusion. In addition, a Trajectory-Constrained Rectifier (TCR) regularizes stochastic sampling via a pixel-latent closed loop, rectifying intermediate predictions with source-referenced structures and re-encoding them to stabilize the denoising trajectory and recover fine details suppressed by latent compression. Experiments across multiple datasets and degradation settings show consistent improvements in restoration quality and fusion fidelity, with strong generalization under complex and compounded degradations.