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5 August 2009 An image fusion algorithm based on regional Kullback-Leibler entropy and nonsubsampled contourlet transform
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Abstract
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shaopeng Liu, Qun Hao, Yong Song, and Yao Hu "An image fusion algorithm based on regional Kullback-Leibler entropy and nonsubsampled contourlet transform", Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73833V (5 August 2009); https://doi.org/10.1117/12.835515
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