Deep　learning-based　methods　have　achieved　significant　success　in　the　field　of　image　fusion,　where　the　design　of　network　architectures　plays　a　crucial　role　in　the　fusion　task.　However,　most　deep　learning　fusion　architectures　still　operate　as　black　boxes　and　lack　awareness　of　frequency　domain　information.　To　enhance　the　interpretability　of　fusion　tasks　and　effectively　utilize　the　frequency　domain　information　from　source　images　while　ensuring　the　generation　of　high-quality　fused　images,　we　propose　a　novel　deep　model-driven　network　for　infrared　and　visible　image　fusion　guided　by　diffusion　priors.　The　proposed　algorithm　generates　a　distribution　of　multi-channel　input　data　through　a　diffusion　process.　Features　produced　by　the　denoiser　serve　as　knowledge　priors,　guiding　a　custom　model　that　leverages　frequency　domain　knowledge　to　reconstruct　high-quality　fused　images.　Specifically,　unlike　some　traditional　fusion　networks,　our　model　can　retain　multi-channel　data　at　the　input　stage　rather　than　just　single-channel　spatial　information.　It　uses　the　multi-channel　features　generated　by　the　diffusion　model　as　priors　to　guide　the　fusion　process.　Moreover,　we　innovatively　design　a　frequency-domain-based　objective　function　to　guide　the　fusion　process,　constructing　a　frequency-domain　learning　module　to　simulate　an　interpretable　deep　model-driven　network.　Additionally,　a　task-driven　loss　function　is　developed　to　ensure　the　quality　of　the　fused　images.　Extensive　experimental　evaluations　across　seven　diverse　datasets　(e.g.,　MSRS,　M3FD,　RoadSence,　TNO,　Havard)　and　multiple　scenarios　demonstrate　that　the　proposed　algorithm　significantly　outperforms　9　state-of-the-art　methods.　Specifically,　it　delivers　superior　fusion　results　on　eight　metrics　(e.g.,　EN,　SF,　VIF)　with　notable　improvements　in　interpretability　and　robustness,　as　validated　through　comprehensive　experiments　on　these　seven　benchmark　datasets.　©　2025