Image fusion based on complex contourlet transform and nonnegative matrix factorization

Abstract

An image fusion method combining complex contourlet transform (CCT) with nonnegative matrix factorization (NMF) is proposed in this paper. After two images are decomposed by CCT, NMF is applied to their highand low-frequency components, respectively, and finally an image is synthesized. Subjective-visual-quality of the image fusion result is compared with those of the image fusion methods based on NMF and the combination of wavelet /contourlet /nonsubsampled contourlet with NMF. The experimental results are evaluated quantitatively, and the running time is also contrasted. It is shown that the proposed image fusion method can gain larger information entropy, standard deviation and mean gradient, which means that it can better integrate featured information from all source images, avoid background noise and promote space clearness in the fusion image effectively.

Keywords

image fusion / complex contourlet transform / nonnegative matrix factorization

References

[1] Ardeshir Goshtasby A., Nikolov S. Image fusion: Advances in the state of the art [J]. Information Fusion, 2007, 8(2): 114-118.

[2] Sabalan D., Hassan Ghassemian. MRI and PET image fusion by combining IHS and retina-inspired models [J]. Information Fusion, 2010, 11(2): 114-123.

[3] Lei Y., Jiang D., Yang Xiaohuan. Application of image fusion in urban expanding detection [J]. Journal of Geomatics, 2007, 32(3): 4-5.

[4] Tu T. M., Cheng W. C., Chang C. P., et al. Best tradeoff for high-resolution image fusion to preserve spatial details and minimize color distortion [J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(2): 302-306.

[5] Wang Z., Ziou D., Armenakis C., et al. A comparative analysis of image fusion methods[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(6): 1391-1402.

[6] Li S., Yang Bin. Multifocus image fusion by combining curvelet and wavelet transform[J]. Pattern Recognition Letters, 2008, 29(9): 1295-1301.

[7] Do M. N., Vetterli M. The contourlet transform: An efficient directional multiresolution image representation[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2091 2106

[8] Chen Dipeng, Li Qi. The use of complex contourlet transform on fusion scheme[C]. In: Proceedings of World Academy of Science, Engineering and Technology. France, 2005. 342–347.

[9] Selesnick I. W., Baraniuk R. G., Kingsbury N. C. The dualtree complex wavelet transform [J]. IEEE Signal Processing Magazine, 2005, 22(6): 123-151.

[10] Lee D. D., Seung H. S. Learning the parts of object by nonnegative matrix factorization [J]. Nature, 1999, 401(6755): 788-791.

[11] Lee D. D., Seung H. S. Algorithms for nonnegative matrix factorization [J]. Advances in Neural and Information Processing Systems, 2001, 13, 556-562.

[12] Phoong S. M., Kim C. W., Vaidyanathan P. P., et al. A new class of two-channel biorthogonal filter banks and wavelet bases[J]. IEEE Transactions on Signal Processing, 1995, 43(3): 649-665.

[13] Zhang Junying, Wei Le, Miao Qiguang et al. Image fusion based on nonnegative matrix factorization [C]. In: International Conference on Image Processing. Singapore, 2004. 973–976.

[14] Ye Youshi, Zhao Baojun. Research on image fusion method based on wavelet transform and NMF [C]. In: Second International Workshop on Computer Science and Engineering. Qingdao, China, 2009. 82–86.

[15] Li M., Li Y., Wang H., et al. Image fusion algorithm based on NSCT and non-negative matrix factorization [J]. Computer Engineering and Applications, 2010, 46(8): 21-24.