Image Fusion Method Based on Universal Hidden Markov Tree Model in the Contourlet Domain

Xi Cai; Guang Han; Jin Kuan Wang

doi:10.4028/www.scientific.net/AMR.684.491

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 684Image Fusion Method Based on Universal Hidden...

Image Fusion Method Based on Universal Hidden Markov Tree Model in the Contourlet Domain

Abstract:

Considering the statistical characteristics of contourlet coefficients of images, we propose an image fusion method based on the universal contourlet hidden Markov tree (uHMT) model. A salience measure and a match measure are presented according to the probability of a contourlet coefficient belonging to the high state of the uHMT model which needs no training. Experimental results prove the effectiveness of our method in the field of visual quality and objective evaluations.

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Advanced Materials Research (Volume 684)

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491-494

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https://doi.org/10.4028/www.scientific.net/AMR.684.491

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April 2013

Authors:

Xi Cai, Guang Han, Jin Kuan Wang

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Contourlet Transform, Image Fusion, Universal Hidden Markov Tree Model

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References

[1] D. D. Y. Po and M. N. Do. Directional Multiscale Modeling of Images Using the Contourlet Transform. IEEE Trans. Image Process., 2006, 15: 1610-1620.

DOI: 10.1109/tip.2006.873450

Google Scholar

[2] X. Cai and W. Zhao. Discussion upon Effects of Contourlet Lowpass Filter on Contourlet-Based Image Fusion Algorithms. Acta Autom. Sin., 2009, 35: 258–266.

DOI: 10.3724/sp.j.1004.2009.00258

Google Scholar

[3] Q. G. Miao and B. S. Wang. A Novel Image Fusion Method Using Contourlet Transform. IEEE Int. Conf. Commun. Circuits Sys., 2006: 548–552.

Google Scholar

[4] Y. A. Zheng, C. S. Zhu, J. S. Song, and X. H. Zhao. Fusion of Multi-band SAR Images Based on Contourlet Transform. IEEE Int. Conf. on Inf. Acquis., 2006: 420–424.

DOI: 10.1109/icia.2006.305749

Google Scholar

[5] L. Yang, B. L. Guo, and W. Ni. Multifocus Image Fusion Algorithm Based on Region Statistics in Contourlet Domain. J. Xi'an Jiaotong Univ., 2007, 41: 448–452.

DOI: 10.1109/icig.2007.135

Google Scholar

[6] J. K. Romberg, H. Choi, and R. G. Baraniuk. Bayesian Tree-Structured Image Modeling Using Wavelet-Domain Hidden Markov Models. IEEE Trans. Image Process., 2001, 10: 1056-1068.

DOI: 10.1109/83.931100

Google Scholar

[7] M. S. Crouse, R. D. Nowak, and R. G. Baraniuk. Wavelet-Based Statistical Signal Processing Using Hidden Markov Models. IEEE Trans. Signal Process., 1998, 46: 886–902.

DOI: 10.1109/78.668544

Google Scholar