In　traditional　infrared　and　visible　image　fusion　algorithms,　some　research　problems　such　as　inadequate　detail　texture　information　and　insufficient　edge　information　retention.　Therefore,　a　non-subsampled　shearlet　transform　(NSST)　image　fusion　method　based　on　fractional　saliency　detection　and　improved　quantum　fireworks　algorithm　is　proposed.　First,　an　NSST　decomposition　is　performed　for　infrared　and　visible　images,　and　the　saliency　detection　is　also　executed　on　the　basis　of　the　fractional　differential　enhancement　for　low-frequency　components,　and　then　fusion　is　carried　out　according　to　the　rules　of　the　saliency　map　matching　degree.　The　high-frequency　subbands　are　merged　by　the　gradient　variation　and　gray　difference　weighting　strategy.　Second,　the　quantum　fireworks　algorithm　is　improved,　and　the　high-　and　low-frequency　fusion　parameters　are　optimized　by　the　improved　quantum　fireworks　algorithm.　Finally,　the　best　fusion　image　can　be　generated.　The　experiment　results　showed　that　the　saliency　detection　based　on　fractional　differential　enhancement　can　achieve　good　visual　saliency.　Moreover,　the　improved　quantum　fireworks　algorithm　has　strong　optimization　ability　and　high　convergence　efficiency.　As　a　result,　the　fusion　image　obtained　by　the　proposed　method　effectively　integrates　the　detailed　information　into　the　infrared　and　visible　images.　Compared　with　the　existing　methods,　the　proposed　method　realizes　a　better　fusion　effect　with　strong　self-adjustment　ability　without　any　human　intervention.　©　2021　Science　Press.　All　right　reserved.