Image fusion algorithm based on neighbors and cousins information in nonsubsampled contourlet transform domain

Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy and directional expansion for images. Compared with the foremost contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. In addition, coefficients of NSCT are...

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Bibliographic Details
Main Authors: Xiao-Bo Qu, Guo-Fu Xie, Jing-Wen Yan, Zi-Qian Zhu, Ben-Gang Chen
Format: Conference Proceeding
Language:English
Subjects:
contourlet transform
Filter bank
Image analysis
Image fusion
Laboratories
Pattern analysis
Pattern recognition
sparse representation
Wavelet analysis
Wavelet domain
wavelet transform
Wavelet transforms
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Summary:Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy and directional expansion for images. Compared with the foremost contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. In addition, coefficients of NSCT are dependent on their neighborhood coefficients in the local window and cousin coefficients in directional subbands. In this paper, region energy and cousin correlation are defined to represent the neighbors and cousins information, respectively. Salience measure, as the combination of region energy and cousin correlation, is defined to obtain fused coefficients in the high-frequency NSCT domain. First, source images are decomposed into subimages via NSCT. Secondly, salience measure is computed. Thirdly, salience measure-maximum-based rule and average rule are employed to obtain high-frequency and low-frequency coefficients, respectively. Finally, fused image is reconstructed by inverse NSCT. Experimental results show that the proposed algorithm outperforms wavelet-based fusion algorithms and contourlet transform-based fusion algorithms.
ISSN:2158-5695
DOI:10.1109/ICWAPR.2007.4421745