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

Xiaobo Qua, Jingwen Yanb, Hongzhi Xiaob, Ziqian Zhuc

Department of Electronic Science, Xiamen University, Xiamen 361005, P.R.China
Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, P. R. China
Research Institute of Chinese Radar Electronic Equipment, Wuxi 214063, P.R.China

Xiaobo Qu's Email: quxiaobo <at> xmu.edu.cn or quxiaobo2009 <at> gmail.com.


Project-Image Fusion:

To download the source images, please click on the name of images or hyperlink of website. Just try it...
Multifocus images: clockA.tif and clockB.tif ;disk1.tif and disk2.tif;mulfocus1.tif and mulfocus2.tif
Visible and infrared images: forestA.jpg and forestB.jpg
Or you can visit http://www.imagefusion.org for more source images.
To download the matlab code for objective evaluation criteria of image fusion, please click on the name of criteria. Just try it...
Mutual information & Qab/f


Abstract:

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 employed 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 term of objective criteria and visual appearance.(download this paper )
Key words: Contourlet, pulse coupled neural networks (PCNN), wavelet, image fusion, multiscale transform


Fig.1 Schematic diagram of NSCT-SF-PCNN fusion algorithm

fig


Conclusion:

In this paper, a spatial frequency motivated PCNN in NSCT domain, NSCT-SF-PCNN, is proposed. The flexible multiresolution, anisotropy, and directional expansion for images of NSCT are associated with global coupling and pulse synchronization characteristic of PCNN. Furthermore, a spatial frequency motivated PCNN, rather than pure use of coefficients value in traditional PCNN in image processing, is presented. Experiments on MSD methods, activity-level measurements, and typical PCNN-based algorithms demonstrate that the proposed NSCT-SF-PCNN is successful in multifocus image fusion and visible and infrared image fusion.


Acknowledgements:

The authors would like to thank Mr. David Dwyer of Octec Ltd, Dr. Lex Toet of TNO Human Factors and Dr. Oliver Rockinger for providing the images used in this work and also thank Hu Chang-Wei for his help in the preparation of the manuscript. Some of the images are available from http://www.imagefusion.org or you can contact qxb xmu@yahoo.com.cn for the images.

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