The　fusion　of　infrared　and　visible　light　aims　to　combine　the　advantages　of　infrared　highlighting　thermal　targets　and　the　rich　textures　of　visible　light,　to　produce　a　fused　image　that　is　both　texture-rich　and　has　prominent　targets.　However,　under　extreme　conditions　such　as　heavy　fog　and　overexposure,　directly　fusing　low-quality　visible　light　images　with　infrared　images　may　retain　excessive　redundant　information　in　these　areas.　Therefore,　this　paper　proposes　a　method　for　achieving　adaptive　fusion　under　extreme　conditions,　called　SPFusion.　Specifically,　the　network　is　guided　by　a　quality　assessment　module　and　advanced　visual　tasks　such　as　segmentation,　classification,　and　reconstruction,　to　learn　an　adaptive　balance　point　for　rationally　distributing　infrared　and　visible　light　information.　Additionally,　a　progressive　fusion　strategy　and　a　modality　complementarity　module　are　employed　to　fully　integrate　information　from　each　modality.　Consequently,　the　network　can　autonomously　allocate　visible　and　infrared　information　based　on　image　quality　and　semantics.　Extensive　experiments　have　demonstrated　that　SPFusion　achieves　state-of-the-art　(SOTA)　performance　on　the　TNO,　MSRS,　LLVIP,　and　RoadScene　datasets,　and　enhances　the　performance　of　advanced　visual　tasks　such　as　object　detection.　The　code　is　available　as　open　source　at　linshenj/SPFusion　(github.com).