PSDFusion: Prior-Guided Feature Specialization with Cross-Paradigm Distillation for Infrared-Visible Image Fusion

Infrared-visible image fusion aims to integrate thermal saliency with visible details while preserving representations useful for downstream perception. Existing prior-guided methods often inject semantic priors into shared multimodal features and transfer teacher knowledge mainly through Euclidean feature matching, which may interfere with low-level visual representation learning and restrict heterogeneous knowledge transfer. We propose PSDFusion, a Prior-guided Specialization and Distillation framework that separates visual specialization from semantic conditioning and enriches student representations across complementary geometries. Specifically, PSDFusion first constructs local-detail and context-preserving representations through asymmetric transformations and cross-gating, followed by text-guided semantic conditioning. It further combines Euclidean and hyperbolic-aware student representations before aligning their channel dependencies with multi-level teacher features in a unified correlation space. Experiments on four infrared-visible fusion benchmarks and two downstream perception tasks against 12 recent baselines demonstrate competitive fusion quality, cross-dataset generalization, and strong task utility. These results support feature specialization and cross-paradigm distillation as complementary mechanisms for balancing visual fidelity and semantic representation learning. The code is publicly available at https://anonymous.4open.science/r/PSDFusion-0CC1.