In order to solve the traditional image fusion deficiencies,such as excessive edge smoothing,loss texture details,low contrast,non-prominent target and missing source image information,this paper proposes an infrared and visible dual-band image fusion algorithm based on non-subsampled shearlet transform (NSST).Firstly,the source infrared and visible images are enhanced through adaptive guided filter (AGF).Secondly,the infrared and visible images are decomposed into low and high frequency components by NSST,respectively.Then,the low frequency components are fused by using the local adaptive intensity (LAI) rule,while high frequency components are fused by using dual channel adaptive pulse coupled neural network (DCAPCNN).Finally,the fused image is reconstructed by using the inverse NSST.Experimental results show that the proposed method has advantages in appropriate contrast,reserving the infrared target characteristic,including more background edge and texture detail information,and fusion image with high signal-noise ratio,the infrared and visible image advantage are effectively combined,compared with the existing traditional and deep learning fusion algorithms,the proposed algorithm achieves better experimental results,with superior performance in both subjective visual perception and objective indicator evaluations.