CLC number: TP317.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 1
Clicked: 5927
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.
@article{title="Performance measure for image fusion considering region information",
author="LIU Gang, LÜ Xue-qin",
journal="Journal of Zhejiang University Science A",
volume="8",
number="4",
pages="559-562",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A0559"
}
%0 Journal Article
%T Performance measure for image fusion considering region information
%A LIU Gang
%A LÜ
%A Xue-qin
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 4
%P 559-562
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A0559
TY - JOUR
T1 - Performance measure for image fusion considering region information
A1 - LIU Gang
A1 - LÜ
A1 - Xue-qin
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 4
SP - 559
EP - 562
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A0559
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.
[1] Blum, R.S., 2005. Minimax Robust Image Fusion Using an Estimation Theory Approach. Proc. 8th International Conference on Information Fusion, 1:461-468.
[2] Gemma, P., 2002. A General Framework for Multiresolution Image Fusion: From Pixels to Regions. Technical Report PNA-R0211, ISSN 1386-3711. Center for Mathematics and Computer Science (CWI), Amsterdam, the Netherlands.
[3] Gemma, P., Henk, H., 2003. A New Quality Metric for Image Fusion. Proc. IEEE International Conference on Image Processing, 3:173-176.
[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.
Open peer comments: Debate/Discuss/Question/Opinion
<1>