Abstract: Aiming at the false edges and artifacts resulted by the existing fusion rules,a new fusion method based on sum-modified Laplacian (SML) and pulse coupled neural network (PCNN) in non-sampled contourlet transform (NSCT) domain was proposed.Firstly,the source images are decomposed into low frequency sub bands which include basic information and multiple high frequency sub bands with detail information via NSCT.Then,the SML of multi-scale coefficients are calculated and used to fuse the low frequency sub-band coefficients.For the high frequency sub-band coefficients,the calculated SML is taken as the input motivation of the PCNN,and the relative output fire map is adopted to select the pixels in the sub-band images.Finally,the dealt coefficients are reconstructed by the NSCT to get the fused image.The experimental results show that the proposed algorithm excellently improves the focus clarity.Compared with the existing algorithms such as SIDWT,DTCWT,NSCT and PCNN-based method,the objective indexes of mutual information,structural similarity and transferred edge information have been increased.

Key words: Multi-focus image,Image fusion,SML,PCNN,NSCT