DOI: 10.3724/SP.J.1004.2008.01508

Acta Automatica Sinica (自动化学报) 2008/34:12 PP.1508-1514

Image Fusion Algorithm Based on Spatia Frequency-Motivated Pulse Coupled Neural Networks in Nonsubsampled Contourlet Transform Domain

Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy, and directional expansion for images.Compared with the original contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. Pulse coupled neural networks (PCNN) is a visual cortex-inspired neural network and characterized by the global coupling and pulse synchronization of neurons. It has been proven suitable for image processing and successfully employed in image fusion.In this paper, NSCT is associated with PCNN and used in image fusion to make full use of the characteristics of them.Spatial frequency in NSCT domain is input to motivate PCNN and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed algorithm outperforms typical wavelet-based, contourlet-based, PCNN-based, and contourlet-PCNN based fusion algorithms in terms of objective criteria and visual appearance.

Key words:Contourlet, pulse coupled neural networks (PCNN), wavelet,image fusion, multiscale transform

ReleaseDate:2014-07-21 14:28:56

Funds:Supported by National Natural Science Foundation of China(60472081) and Navigation Science Foundation of China (05F07001)

1 Hall D L, Llinas J. An introduction to multisensor data fusion.Proceedings of the IEEE,1997,85(1):6-23

2 Zhang Z, Blum R S. A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application.Proceedings of the IEEE,1999,87(8):1315-1326

3 Le P E, Mallat S. Sparse geometric image representation with bandelets.IEEE Transactions on Image Processing,2005,14(4):423-438

4 Do M N, Vetterli M. The contourlet transform: an efficient directional multiresolution image representation.IEEE Transactions on Image Processing,2005,14(12):2091-2106

5 Qu X B, Yan J W, Xie G F, Zhu Z Q, Chen B G. A novel image fusion algorithm based on bandelet transform.Chinese Optics Letters,2007,5(10):569-572

6 Choi M, Kim R Y, Nam M R, Kim H O. Fusion of multispectral and panchromatic satellite images using the curvelet transform.IEEE Geoscience and Remote Sensing Letters,2005,2(2):136-140

7 Qu X B, Xie G F, Yan J W, Zhu Z Q, Chen B G. Image fusion algorithm based on neighbors and cousins information in nonsubsampled contourlet transform domain. In: Proceedings of International Conference on Wavelet Analysis and Pattern Recognition. Beijing, China: IEEE, 2007. 1797-1802

8 Zheng Yong-An, Song Jian-She, Zhou Wen-Ming, Wang Rui-Hua. False color fusion for multi-band SAR images based on contourlet transform.Acta Automatica Sinica,2007,33(4):337-341

9 Fang Yong, Liu Sheng-Peng. Infared Image Fusion Algorithm Based on Contourlet Transform and Improved Pulse Coupled Neural Networks, China Patent 1873693A, December 2006 (in Chinese)

10 Da Cunha A L, Zhou J P, Do M N. The nonsubsampled contourlet transform: theory, design, and applications.IEEE Transactions on Image Processing,2006,15(10):3089-3101

11 Eckhorn R, Reitboeck H J, Arndt M, Dicke P. Feature linking via synchronization among distributed assemblies: simulations of results from cat visual cortex.Neural Computation,1990,2(3):293-307

12 Johnson J L, Padgett M L. PCNN models and applications.IEEE Transactions on Neural Networks,1999,10(3):480-498

13 Broussard R P, Rogers S K, Oxley M E, Tarr G L. Physiologically motivated image fusion for object detection using a pulse coupled neural network.IEEE Transactions on Neural Networks,1999,10(3):554-563

14 Li M, Cai W, Tan Z. Pulse coupled neural network based image fusion. In: Proceedings of the 2nd International Symposium on Neural Networks. Chongqing, China: Springer, 2005.741-746

15 Li W, Zhu X F. A new algorithm of multi-modality medical image fusion based on pulse-coupled neural networks. In: Proceedings of International Conference on Advances in Natural Computation. Changsha, China: Springer, 2005. 995-1001

16 Xu B C, Chen Z. A multisensor image fusion algorithm based on PCNN. In: Proceeding of the 5th World Congress on Intelligent Control and Automation. Hangzhou, China: IEEE, 2004. 3679-3682

17 Qu Xiao-Bo, Yan Jing-Wen, Zhu Zi-Qian, Chen Ben-Gang. Multi-focus image fusion algorithm based on regional firing characteristic of pulse coupled neural networks. In: Proceedings of International Conference on Bio-Inspired Computing: Theories and Applications. Zhengzhou, China: Publishing House of Electronics Industry, 2007. 563-565

18 Eskicioglu A M, Fisher P S. Image quality measures and their performance.IEEE Transactions on Communications,1995,43(12):2959-2965

19 Qu G H, Zhang D L, Yan P F. Information measure for performance of image fusion.Electronics Letters,2002,38(7):313-315

20 Petrovic V, Xydeas C. On the effects of sensor noise in pixel-level image fusion performance. In: Proceedings of the 3rd International Conference on Image Fusion. Paris, France: IEEE, 2000. 14-19