P²Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation

Infrared-visible image fusion (IVIF) is pivotal for multimodalperception, yet reconciling the inherent information disparity betweenthermal and textural features remains a fundamental challenge. Exist-ing prior-guided methods often rely on static constraints that induceoptimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.g., CLIP/DINO), which frequently fail toexploit the intrinsic modality characteristics essential for high-fidelityfusion. To address these issues, we propose P²Fusion, a prior-guideddistillation-based framework that reformulates IVIF via dual intrinsicprompts. Instead of imposing hard-coded penalties, we distill image-intrinsic priors, thermal saliency and spatial quality—into learnable,dynamic regulators. Specifically, a Teach-to-Fuse mechanism providesdual-granularity progressive guidance, coupled with a Gated DynamicExpert Recalibration (GDER) module for decoupled feature refinement.This design enables the network to adaptively mediate modal competi-tion through expert specialization. Extensive experiments demonstratethat P²Fusion achieves state-of-the-art performance across five main-stream datasets. Notably, our framework demonstrates consistent per-formance advantages in fusion quality, achieving state-of-the-art resultsin 14 out of 20 key evaluation metrics across 5 benchmarks. Further-more, it effectively contributes to the robustness of downstream percep-tion, such as +3.2% mAP on MSRS, +0.5% mAP on M3FD and +0.9%mAP on DroneVehicle for object detection. Our code will be available athttps://github.com/YiShi99/P2Fusion.