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Research on Image Fusion Based on Pyramid Decomposition

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Abstract:

Different objects could be analyzed by use of pyramid decomposition of image. Image fusion algorithm based on pyramid decomposition of image is multi-scale, multi-resolution method. Its process is completed on different scale, different resolution and different decomposition layer. Compared with spatial fusion method, fusion effects improve obviously. In this paper, principal of pyramid decomposition and process were introduced, and simulation results of image fusion based on Laplacian pyramid, gradient pyramid, ration pyramid and contrast shows that image fusion based on pyramid decomposition is improve obviously.

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Periodical:

Advanced Materials Research (Volumes 860-863)

Pages:

2855-2858

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.860-863.2855

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Online since:

December 2013

Authors:

Ming Jing Li, Yu Bing Dong, Xiao Li Wang

Keywords:

Contrast Pyramid, Gradient Pyramid, Image Fusion, Laplacian Pyramid, Ratio Pyramid

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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References

[1] Mallat, S. G, A theory for multiresolution signal decomposition: The wavelet representation, in: IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, 1989, S. 674-693.

DOI: 10.1109/34.192463

Google Scholar

[2] R.C. Luo and M. G. Kay, Data Fusion and Sensor Integration: State of the Art 1990s, in: M. A. Abidi. and R. C. Gonzalez (eds): Data Fusion in Robotics and Machine Intelligence.

Google Scholar

[3] G. Pajares, J. Cruz, A wavelet-based image fusion tutorial, Pattern Recognition 37 (9) (2004).

DOI: 10.1016/j.patcog.2004.03.010

Google Scholar

[4] S. G. Mallat, A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-11, July 1989, pp.674-693.

DOI: 10.1109/34.192463

Google Scholar

[5] M. Unser, Texture Classification and Segmentation using Wavelet Frames, IEEE Trans Image Proc., vol. IP-4, November 1995, pp.1549-1560.

DOI: 10.1109/83.469936

Google Scholar

[6] J.J. Lewis, R.J. Ocallaghan, S.G. Nikolov, Pixel- and region-based image fusion with complex wavelets, Information Fusion 8 (2) (2007) 119–130.

DOI: 10.1016/j.inffus.2005.09.006

Google Scholar

[7] R. Sabari Banu , Medical Image Fusion by the analysis of Pixel LevelMulti-sensor Using Discrete Wavelet Transform , Proceedings of the National Conference on Emerging Trends in Computing Science, 2011, pp.291-297.

Google Scholar

[8] Z. Wang , Y . Tie, and Y . Liu , Design and Implementation of Image Fusion System, International Conference on Computer Application and System Modeling (ICCASM), 2010, pp.140-143.

DOI: 10.1109/iccasm.2010.5622856

Google Scholar

[9] M. Kokar and K. Kim, Review of multisensor data fusion architectures, in Proceeding of the IEEE International Symposium on Intelligent Control, August 1993, p.261–266.

DOI: 10.1109/isic.1993.397703

Google Scholar