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Proceedings Paper

An image fusion algorithm based on regional Kullback-Leibler entropy and nonsubsampled contourlet transform
Author(s): Shaopeng Liu; Qun Hao; Yong Song; Yao Hu
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Paper 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.

Paper Details

Date Published: 5 August 2009
PDF: 8 pages
Proc. SPIE 7383, International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, 73833V (5 August 2009); doi: 10.1117/12.835515
Show Author Affiliations
Shaopeng Liu, Beijing Institute of Technology (China)
Qun Hao, Beijing Institute of Technology (China)
Yong Song, Beijing Institute of Technology (China)
Yao Hu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7383:
International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications
Jeffery Puschell; Hai-mei Gong; Yi Cai; Jin Lu; Jin-dong Fei, Editor(s)

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