An Effective Image Fusion Method Based on Nonsubsampled Contourlet Transform and Pulse Coupled Neural Network

Li Juan Ma; Chun Hui Zhao

doi:10.4028/www.scientific.net/AMR.756-759.3542

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759An Effective Image Fusion Method Based on...

An Effective Image Fusion Method Based on Nonsubsampled Contourlet Transform and Pulse Coupled Neural Network

Abstract:

In order to solve the problem of spectral distortion and the fuzzy texture in visible and infrared image fusion technology, a novel visible and infrared image fusion method based on the Nonsubsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Networks (PCNN) is proposed in this paper. First, we gain three components of visible image, luminance I, chrominance H and saturation S, using the IHS transform. Then, we gain three coefficients, low frequency sub-band, passband sub-band and high frequency coefficient by decomposing the component I and infrared image with the help of the NSCT. Next, we use weighted-sum method to fuse the low frequency sub-band and PCNN method to fuse the other sub-band coefficient respectively. At last, we gain the fusion image by using the inverse IHS transform on the fusion component I gained by the inverse NSCT transform. Experiments show that our method have better fusion quality and can be more better to keep the visible spectral and detail information than some traditional methods such as, Laplace method, Wavelet method and Lifting Wavelet method.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

3542-3548

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.3542

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Online since:

September 2013

Authors:

Li Juan Ma, Chun Hui Zhao

Keywords:

IHS Transform, Image Fusion, Nonsubsampled Contourlet Transform (NSCT), Pulse Coupled Neural Networks Transform

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References

[1] L. D. Cunha, J. P. Zhou and N. D. Minh, The nonsubsampled contourlet transform: Theory, design, and applications, IEEE Transl. Image Processing, vol. 15, no. 10, p.3089–3101, October (2006).

DOI: 10.1109/tip.2006.877507

Google Scholar

[2] R. Eslami and H. Radha, Translation-invariant contourelt transform and its application to image denoising, IEEE Transl. Image Processing, vol. 15, no. 11, p.3362–3374, November (2006).

DOI: 10.1109/tip.2006.881992

Google Scholar

[3] R. Eckhom, H. J. Reitboeck, M. Arndt and P. Dicke, Feature linking via synchronization among distributed assemblies, Mit Press Journals, vol. 2, no. 3, p.293–307, (1990).

DOI: 10.1162/neco.1990.2.3.293

Google Scholar

[4] B. C. Xu and Z. Chen, A multi-sensor image fusion algorithm based on PCNN, WCICA2004, p.3679–3682, June (2004).

Google Scholar

[5] C. X. Lu, Q. Pan, Y. M. Cheng, "New image fusion method based on HIS and wavelet.

Google Scholar

[6] transform, " Application Research of Computers, vol. 25, no. 2, p.3690–3695, February (2008).

Google Scholar

[7] L. Tang, F. Zhao and Z. G. Zhao, The nonsubsampled contourlet trams-form for image fusion, ICWAPR07, p.305–310, November (2007).

Google Scholar

[8] G. F. Xie, J. W. Yan, Z. Q. Zhu and B. G. Chen, Image fusion algorithm based on neighbors and cousins information in nonsubsampled contourlet tramsform, ICWAPR07, p.1797–1802, November (2007).

DOI: 10.1109/icwapr.2007.4421745

Google Scholar

[9] R. H. Bamberger, A filter bank for the directional de-composition of images: Theory and design, IEEE Transl. Signal Processing, vol. 40, no. 4, p.882–893, (1992).

DOI: 10.1109/78.127960

Google Scholar

[10] J. L. Johnson, D. Ritter. Observation of periodic waves in a pulse-coupled neural network, Optics Lett, vol. 18, no. 15, p.1253–1255, (1993).

DOI: 10.1364/ol.18.001253

Google Scholar

[11] H. Chen and Y. Y. Liu, An infrared image fusion algorithm based on lifting wavelet transform, Laser and Infrared, vol. 39, no. 1, January (2009).

Google Scholar