Journal
INFORMATION FUSION
Volume 12, Issue 2, Pages 74-84Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2010.03.002
Keywords
Image fusion; Multi-resolution transform; Wavelet; Curve let; Contour let
Funding
- National Natural Science Foundation of China [60871096, 60835004]
- Ph.D. Programs Foundation of Ministry of Education of China [200805320006]
- Chinese Ministry of Education [2009-120]
- National Laboratory of Pattern Recognition
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Image fusion combines information from multiple images of the same scene to get a composite image that is more suitable for human visual perception or further images-processing tasks. In this paper, we compare various multi-resolution decomposition algorithms, especially the latest developed image decomposition methods, such as curvelet and contourlet, for image fusion. The investigations include the effect of decomposition levels and filters on fusion performance. By comparing fusion results, we give the best candidates for multi-focus images, infrared-visible images, and medical images. The experimental results show that the shift-invariant property is of great importance for image fusion. In addition, we also conclude that short filter usually provides better fusion results than long filter, and the appropriate setting for the number of decomposition levels is four. (C) 2010 Elsevier B.V. All rights reserved.
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