Medium-　and　long-wave　infrared　image　fusion　has　problems　such　as　overemphasizing　detail　retention,　which　often　weakens　the　presence　of　thermal　information,　poor　contrast　of　fused　images,　and　large　noise,　so　a　medium-　and　long-wave　image　fusion　method　based　on　improved　non-subsample　shearlt　transform　(NSST)　is　proposed.　Firstly,　the　image　processing　of　mid-wave　infrared　and　long-wave　infrared　images　is　carried　out　in　a　targeted　manner,　and　the　pixel　values　of　the　target　and　background　area　are　adjusted　by　using　the　adaptive　contrast　enhancement　algorithm　to　adjust　the　pixel　values　of　the　mid-wave　infrared　image,　so　as　to　achieve　the　target　enhancement　effect　by　expanding　the　relative　pixel　difference　between　the　thermal　target　and　the　background　area.　Secondly,　the　average　curvature　filtering　and　Gaussian　filtering　are　used　to　decompose　the　source　image　into　detail　layer,　structure　layer　and　area　layer.　The　energy　differential　feature　is　used　to　guide　the　energy　attribute　fusion　strategy　to　fuse　the　regional　layer,　the　structure　layer　adopts　the　maximum　fusion　strategy　to　fuse,　and　the　detail　layer　adopts　the　fusion　strategy　of　directional　contrast.　Finally,　the　three　levels　after　fusion　are　added　to　reconstruct　the　final　fusion　image.　Experimental　results　show　that　the　algorithm　can　effectively　fuse　mid-wave　infrared　and　long-wave　infrared　images,　which　can　not　only　effectively　retain　the　mid-wave　infrared　thermal　radiation　and　heat　information,　but　also　retain　the　edge　detail　expression　ability　in　the　fusion　results　to　a　large　extent.　It　can　be　seen　from　the　subjective　and　objective　evaluation　indicators　that　the　proposed　algorithm　shows　better　fusion　performance　than　other　algorithms.　©　2022　ACM.