A Novel Fusion Algorithm for Visible and Infrared Image using Non-subsampled Contourlet Transform and Pulse-coupled Neural Network

Chihiro Ikuta, Songjun Zhang, Yoko Uwate, Guoan Yang, Yoshifumi Nishio

2014

Abstract

An image fusion algorithm between visible and infrared images is significant task for computer vision applications such as multi-sensor systems. Among them, although a visible image is clear perfectly able to be seen through the naked eyes, it is often suffers with noise; while an infrared image is unclear but it has high anti-noise property. In this paper, we propose a novel image fusion algorithm for visible and infrared images using a non-subsampled contourlet transform (NSCT) and a pulse-coupled neural network (PCNN). First, we decompose two original images above mentioned into low and high frequency coefficients based on the NSCT. Moreover, each low frequency coefficients for both images are duplicated at multiple scales, and are processed by laplacian filter and average filter respectively. Finally, we can fuse the normalized coefficients by using the PCNN. Conversely, we can reconstruct a fused image based on the low and high frequency coefficients, which are fused by using the inverse NSCT. Experimental results show that the proposed image fusion algorithm surpasses the conventional and state-of-art image fusion algorithm.

References

  1. da Cunha, A., Zhou, J., and Do, M. (2006). The nonsubsampled contourlet transform: Theory, design, and applications. IEEE Transaction on Image Processing, 15(10):3089-3101.
  2. Eckhorn, R. (1990). Feature linking via synchronization among distributed assemblies: Simulations of result from cat visual cortex. Neural Computation, 2:293- 307.
  3. Ge, Y. and Li, X. (2010). Image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform. Proc. Second International Workshop on Education Technology and Computer Science, 3:27-30.
  4. Jhonson, J. and Padgett, M. (1999). Pcnn models and applications. IEEE Transactions of Neural Network, 10(3):480-498.
  5. Lindblad, T. and Kinser, J. (2005). Image Processing using Pulse-Coupled Neural Networks. Springer Publisher.
  6. Liu, F., Li, J., and Huang, C. (2012). Image fusion algorithm based on simplified pcnn in nonsubsampled contourlet transform domain. International Workshop on Information and Electronics Engineering, 29:1434-1438.
  7. Qu, X., Yan, J., Xiao, H., and Zhu, Z. (2008). Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Automatica Sinica, 34(12):1508-1514.
  8. Wang, M., Peng, D., and Yang, S. (2008). Fusion of multiband sar images based on nonsubsampled contourlet and pcnn. Proc. 4th International Conference on Natural Computation, pages 529-533.
  9. Xu, B. and Chen, Z. (2004). A multisensor image fusion algorithm based on pcnn. Proc. the 5th World Congress on Intelligent Control and Automation, pages 3679- 3682.
  10. Yang, G., Wetering, H. V. D., Hou, M., Ikuta, C., and Liu, Y. (2010). A novel design approach for contourlet filter banks. IEICE Transactions on Information and Systems, E93-D(7):2009-2011.
  11. Yang, S., M. Wang, Y. L., Qi, W., and Jiao, L. (2009). Fusion of multiparametric sar images based on swnonsubsampled contourlet and pcnn. Signal Processing, 89(12):2596-2608.
  12. Zhou, J., da Cunha, A., and Do, M. (2005). Nonsubsampled contourlet transform: Construction and application in enhancement. Proc. International Conference on Image Processing, pages 469-472.
Download


Paper Citation


in Harvard Style

Ikuta C., Zhang S., Uwate Y., Yang G. and Nishio Y. (2014). A Novel Fusion Algorithm for Visible and Infrared Image using Non-subsampled Contourlet Transform and Pulse-coupled Neural Network . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 160-164. DOI: 10.5220/0004732601600164


in Bibtex Style

@conference{visapp14,
author={Chihiro Ikuta and Songjun Zhang and Yoko Uwate and Guoan Yang and Yoshifumi Nishio},
title={A Novel Fusion Algorithm for Visible and Infrared Image using Non-subsampled Contourlet Transform and Pulse-coupled Neural Network},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={160-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004732601600164},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - A Novel Fusion Algorithm for Visible and Infrared Image using Non-subsampled Contourlet Transform and Pulse-coupled Neural Network
SN - 978-989-758-003-1
AU - Ikuta C.
AU - Zhang S.
AU - Uwate Y.
AU - Yang G.
AU - Nishio Y.
PY - 2014
SP - 160
EP - 164
DO - 10.5220/0004732601600164