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CLC number: TP317.4

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Received: 2006-06-12

Revision Accepted: 2006-09-15

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.4 P.559-562

http://doi.org/10.1631/jzus.2007.A0559


Performance measure for image fusion considering region information


Author(s):  LIU Gang, LÜ, Xue-qin

Affiliation(s):  School of Power and Automation Engineering, Shanghai University of Electric Power, Shanghai 200093, China

Corresponding email(s):   lukelg@gmail.com

Key Words:  Image fusion, Information fusion, Image processing


LIU Gang, LÜ Xue-qin. Performance measure for image fusion considering region information[J]. Journal of Zhejiang University Science A, 2007, 8(4): 559-562.

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author="LIU Gang, LÜ Xue-qin",
journal="Journal of Zhejiang University Science A",
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year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0559"
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T1 - Performance measure for image fusion considering region information
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Abstract: 
An objective performance measure for image fusion considering region information is proposed. The measure not only reflects how much the pixel level information that fused image takes from the source image, but also considers the region information between source images and fused image. The measure is meaningful and explicit. Several simulations were conducted to show that it accords well with the subjective evaluations.

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

Reference

[1] Blum, R.S., 2005. Minimax Robust Image Fusion Using an Estimation Theory Approach. Proc. 8th International Conference on Information Fusion, 1:461-468.

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[4] Liu, G., Jing, Z.L., Sun, S.Y., 2006. Multiresolution image fusion scheme based on fuzzy region feature. J. Zhejiang Univ. Sci. A, 7(2):117-122.

[5] MacQueen, J.B., 1967. Some Methods for Classification and Analysis of Multivariate Observations. Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, Berkeley, 1:281-297.

[6] Qu, G.H., Zhang, D.L., Yan, P.F., 2002. Information measure for performance of image fusion. Electron. Lett., 38(7):313-315.

[7] Wang, Z.J., Djemel, Z., Armenakis, C., Li, D., Li, Q.Q., 2005. A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sensing, 43(6):1391-1402.

[8] Xydeas, C.S., Petrovic, V., 2000. Objective image fusion performance measure. Electro. Lett., 36(4):308-309.

[9] Yang, J., Blum, R.S., 2005. Multi-frame Image Fusion Using the Expectation-Maximization Algorithm. Proc. 8th International Conference on Information Fusion, 1:469-471.

[10] 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.

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