CFP last date
20 December 2024
Reseach Article

Objective Criterion for Performance Evaluation of Image Fusion Techniques

by Anjali Malviya, S.G. Bhirud
journal cover thumbnail
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 25
Year of Publication: 2010
Authors: Anjali Malviya, S.G. Bhirud
10.5120/457-761

Anjali Malviya, S.G. Bhirud . Objective Criterion for Performance Evaluation of Image Fusion Techniques. International Journal of Computer Applications. 1, 25 ( February 2010), 57-60. DOI=10.5120/457-761

@article{ 10.5120/457-761,
author = { Anjali Malviya, S.G. Bhirud },
title = { Objective Criterion for Performance Evaluation of Image Fusion Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 25 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 57-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number25/457-761/ },
doi = { 10.5120/457-761 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:48:35.335862+05:30
%A Anjali Malviya
%A S.G. Bhirud
%T Objective Criterion for Performance Evaluation of Image Fusion Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 25
%P 57-60
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The general requirements of an image fusion process are that it should preserve all valid and useful pattern information from the source images, while at the same time not introducing artifacts that could interfere with subsequent analysis. However it is not possible to combine images without introducing some form of distortion. As the image fusion technologies have been developing quickly in a number of applications such as remote sensing, medical imaging, machine vision, and military applications in recent years, the methods that can assess or evaluate the performances of different fusion technologies have been recognized as an urgent requirement. Usually, comparative evaluation by human visual inspection in image fusion is used to assess the relative fusion performance of different fusion schemes. In this paper, various quantitative quality metrics have been implemented to evaluate the performance of the fusion algorithms objectively. Some commonly used image fusion schemes, based on pixel and region based fusion algorithms, and the discrete wavelet transform (DWT), are performed to evaluate the effectiveness of the various metrics.

References
  1. L. J. Chipman, et al, “Wavelets and image fusion”, Proc. Int. Conf. on Image Processing, 248-251, 1995.
  2. R. Oliver, “Image sequence fusion using a shift invariant wavelet transform”, Proc. Int. Conf. on Image Processing, 288-291, 1997.
  3. C. S. Xydeas, and V. Petrovic, "Objective image fusion performance measure " Electron. Lett. 38, pp.308-309, 2002 Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing letters, vol. 9, no.3, pp. 81- 84, March 2002
  4. G. Piella, H. Heijmans, “A new quality metric for image fusion,” IEEE Conference on Image Processing, vol. 3, pp. 173-176, 2003
  5. G. Qu, D. Zhang, P. Yan, “Information measure for performance of image fusion,” Electronics Letters, vol. 38(7), pp. 313-315, IEE, 2002.
  6. Qiang Wang, Yi Shen, Ye Zhang, and Jian Qiu Zhang," Fast Quantitative Correlation Analysis and Information Deviation Analysis for Evaluating the Performances of Image Fusion Techniques", IEEE Transactions on instrument and measurement, vol53,No.5,pp.1441-1447,Oct.2004.
  7. Firooz Sadjadi, "Comparative Image Fusion Analysis", IEEE CVPR'05, 2005.
  8. V. Petrovic, C. Xydeas, “Objective evaluation of signal-level image fusion performance,” Optical Engineering, SPIE, Vol. 44(8), 087003, 2005
  9. Vassilis Tsagaris, and Vassilis Anastassopoulos, "Global measure for assessing image fusion methods ", SPIE Optical Engineering, vol.45, no.2, pp.026201-1-8, Feb.2006
  10. V. Tsagaris, V. Anastassopoulos, “Global measure for assessing image fusion methods,” Optical Engineering, Vol. 45, SPIE, 2006.
  11. V. Petrovic, T. Cootes, “Information Representation for image fusion evaluation,” Proceedings of Fusion, Florence, ISIF, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Image fusion wavelet transform performance evaluation