The　fusion　of　multi-modal　images　to　create　an　image　that　preserves　the　unique　features　of　each　modality　as　well　as　the　features　shared　across　modalities　is　a　challenging　task,　particularly　in　the　context　of　infrared　(IR)-visible　image　fusion.　In　addition,　the　presence　of　polarization　and　IR　radiation　information　in　images　obtained　from　IR　polarization　sensors　further　complicates　the　multi-modal　image-fusion　process.　This　study　proposes　a　fusion　network　designed　to　overcome　the　challenges　associated　with　the　integration　of　low-resolution　IR,　IR　polarization,　and　high-resolution　visible　(VIS)　images.　By　introducing　cross　attention　modules　and　a　multi-stage　fusion　approach,　the　network　can　effectively　extract　and　fuse　features　from　different　modalities,　fully　expressing　the　diversity　of　the　images.　This　network　learns　end-to-end　mapping　from　sourced　to　fused　images　using　a　loss　function,　eliminating　the　need　for　ground-truth　images　for　fusion.　Experimental　results　using　public　datasets　and　remote-sensing　field-test　data　demonstrate　that　the　proposed　methodology　achieves　commendable　results　in　qualitative　and　quantitative　evaluations,　with　gradient　based　fusion　performance　QAB/F,　mutual　information　(MI),　and　QCB　values　higher　than　the　second-best　values　by　0.20,　0.94,　and　0.04,　respectively.　This　study　provides　a　comprehensive　representation　of　target　scene　information　that　results　in　enhanced　image　quality　and　improved　object　identification　capabilities.　In　addition,　outdoor　and　VIS　image　datasets　are　produced,　providing　a　data　foundation　and　reference　for　future　research　in　related　fields.　©　2024　Elsevier　B.V.