Abstract
Considering multi-focus images from the same scene, a fusion method using pulse-coupled neural network in non-subsampled Contourlet transform domain is proposed. The input images are performed to multi-scale and multi-direction NSCT decomposition, then both the low-pass subband coefficients and the band-pass directional subband coefficients are input into PCNN. The ignition mapping images are obtained via the ignition frequency generated by neuron iteration. With the neighborhood approach degree of ignition frequency, corresponding subband coefficients are selected and the fused result is obtained through inverse NSCT. Experimental analysis demonstrates that the proposed method retains clear regions and feature information of multi-focus images on a greater degree and has better fusion performance than other existing methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, Q., Guo, B.: Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing 89(7), 1334–1346 (2009)
Guo, B., Zhang, Q., Hou, Y.: Region-based fusion of infrared and visible images using nonsubsampled contourlet transform. Chinese Optics Letters 6(5), 338–341 (2008)
Wang, Z., Ma, Y.: Medical image fusion using m-PCNN. Information Fusion 9(2), 176–185 (2008)
Qu, X., Yan, J., Xiao, H., et al.: Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Automatica Sinica 34(12), 1508–1514 (2008)
Arthur, L., Cunha, D., Zhou, J.: The nonsubsampled contourlet transform: theory, design and application. IEEE. Transactions on Image Processing 10(15), 3089–3101 (2006)
Yang, X., Jiao, L.: Fusion algorithm for remote sensing images based on nonsubsampled contourlet transform. Acta Automatica Sinica 34(3), 274–281 (2008)
Liu, K., Guo, L., Chang, W.W.: Regional feature selfadaptive image fusion algorithm based on contourlet transform. Acta Optica Sinica 28(4), 681–686 (2008)
Wang, Z., Ma, Y., Cheng, F., et al.: Review of pulse-coupled neural networks. Image and Vision Computing 28(1), 5–13 (2010)
Berg, H., Olsson, R., Lindblad, T., et al.: Automatic design of pulse coupled neurons for image segmentation. Neurocomputing 71(10-12), 1980–1993 (2008)
Miao, Q., Wang, B.: A novel image fusion algorithm based on local contrast and adaptive PCNN. Chinese Journal of Computers 31(5), 875–880 (2008)
Wang, J.-H., Gao, Y.: Multi-sensor data fusion for land vehicle attitude estimation using fuzzy expert system. Data Science Journal 26(4), 127–139 (2005)
Hu, Z., Liu, X.: Method of multi-sensor data fusion based on relative distance. Systems Engineering and Electronics 28(2), 196–198 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this paper
Cite this paper
Jiao, Z., Shao, J., Xu, B. (2012). A Novel Multi-focus Image Fusion Method Using NSCT and PCNN. In: Kim, H. (eds) Advances in Technology and Management. Advances in Intelligent and Soft Computing, vol 165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29637-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-29637-6_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29636-9
Online ISBN: 978-3-642-29637-6
eBook Packages: EngineeringEngineering (R0)