In view of the low edge blurring and l ow definition of traditional image fusion methods,an improved algorithm for visi ble and visible image fusion based on Pulse coupled neural network (PCNN) and in tuitionistic fuzzy set (IFS) is proposed.In this method,the luminance and color information of visible images were separated by IHS,and the luminance I component value was obtained.Then,the I component and the infrared image were decomposed by non-down-sampling contour wave change (NSCT),and the high and low frequency coefficients were obtained,gau ssian membership function and intuitionistic fuzzy set were used to fuse the low frequency parts,and PCNN model was used to fuse the high frequency parts,the I component of the final fusion image was obtained through the inverse change of t he non-lower and non-lower sampled contour waves,and finally the color image w as obtained by the contravariant IHS transformation.Experimental results show th at the final fusion image has more detailed information and the fusion effect is also significantly improved,indicating the superiority of the algorithm.Compare d with other existing infrared and visible image fusion methods,the proposed met hod has significantly improved entropy,edge retention,mutual information,standar d deviation,structural similarity and other indexes of the fusion image,which ef fectively verifies the effectiveness of the proposed algorithm.