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CLC number: TP391.4

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Received: 2005-07-10

Revision Accepted: 2005-12-04

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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.2 P.117~122

http://doi.org/10.1631/jzus.2006.A0117


Multiresolution image fusion scheme based on fuzzy region feature


Author(s):  Liu Gang, Jing Zhong-liang, Sun Shao-yuan

Affiliation(s):  School of Electrical and Information Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; more

Corresponding email(s):   lukelg@sjtu.edu.cn, zljing@mail.sjtu.edu.cn

Key Words:  Image fusion, Image multiscale decomposition, Discrete wavelet frame


Liu Gang, Jing Zhong-liang, Sun Shao-yuan. Multiresolution image fusion scheme based on fuzzy region feature[J]. Journal of Zhejiang University Science A, 2006, 7(2): 117~122.

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author="Liu Gang, Jing Zhong-liang, Sun Shao-yuan",
journal="Journal of Zhejiang University Science A",
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pages="117~122",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0117"
}

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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0117

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T1 - Multiresolution image fusion scheme based on fuzzy region feature
A1 - Liu Gang
A1 - Jing Zhong-liang
A1 - Sun Shao-yuan
J0 - Journal of Zhejiang University Science A
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SP - 117
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2006.A0117


Abstract: 
This paper proposes a novel region based image fusion scheme based on multiresolution analysis. The low frequency band of the image multiresolution representation is segmented into important regions, sub-important regions and background regions. Each feature of the regions is used to determine the region’s degree of membership in the multiresolution representation, and then to achieve multiresolution representation of the fusion result. The final image fusion result can be obtained by using the inverse multiresolution transform. Experiments showed that the proposed image fusion method can have better performance than existing image fusion methods.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Hall, D.L., Llinas, J., 1997. An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1):6-23.

[2] Piella, G., 2002. A General Framework for Multiresolution Image Fusion: From Pixels to Regions. Technical Report PNA-R0211, ISSN 1386-3711, CWI, Amsterdam, The Netherlands.

[3] Qu, G., Zhang, D., Yan, P., 2002. Information measure for performance of image fusion. Electronics Letters, 38(7):313-315.

[4] Rockinger, O., Fechner, T., 1998. Pixel-level image fusion: The case of image sequences. Proceedings of SPIEThe International Society for Optical Engineering, 3374:378-388.

[5] Unser, M., 1995. Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing, 4(11):1549-1560.

[6] Xydeas, C.S., Petrovic, V., 2000. Objective image fusion performance measure. Electronics Letters, 36(4):308-309.

[7] Zhang, Z., Blum, R.S., 1997. A Region-based Image Fusion Scheme for Concealed Weapon Detection. Proc. 31st Annu. Conf. Information Sciences and Systems, Baltimore, MD, p.168-173.

[8] Zhou, J., Civco, D.L., Silander, J.A., 1998. A wavelet transform method to merge Landsat™ and SPOT panchromatic data. International Journal of Remote Sensing, 19(4):743-757.

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