An image fusion algorithm based on regional Kullback-Leibler entropy and nonsubsampled contourlet transform

摘要

A novel image fusion algorithm based on regional Kullback-Leibler entropy analysis and nonsubsampled contourlet transform is proposed in this paper. The equation of Kullback-Leibler entropy is modified at first, and then the modified Kullback-Leibler entropy of the corresponding area of the two source image is calculated. The result of the Kullback-Leibler entropy is clustered to three classes. According to the result of the clustering, different fusion strategies are selected for low frequency subband coefficients. High frequency coefficients are fused using a "local feature-based" rule. Then the fused coefficients are reconstructed to obtain the fused image. Experimental results showed that the proposed algorithm not only improved the visual effect, but also enhanced the contrast and information entropy.