NDLI: Image fusion using D-S evidence theory and ANOVA method

Content Provider	IEEE Xplore Digital Library
Author	Hu Liangmei Gao Jun He Kefeng Xie Zhao
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. & Inf., Hefei Univ. of Technol., China (Hu Liangmei; Gao Jun; He Kefeng; Xie Zhao)
Abstract	Image fusion is an important embranchment in information fusion. In image processing, information fusion techniques are able to significantly reduce uncertainty and inaccuracy in the information obtained from any single source alone. In this paper, a new method based on D-S evidence theory and ANOVA (Analysis Of Variance) method is proposed for image fusion. ANOVA method is employed for detecting potential edges in image. To deal with the uncertain weak edge and the difficulty in threshold selection, D-S evidence theory is then applied. Since D-S evidence theory has the advantage of conveniently representing uncertain information and dealing with information from multi-images, edges to be fused could be preserved as much as possible, which may be useful for further processing, such as image analysis and image understanding. The proposed image fusion method can be applied to fusing different types of images, such as visible and infrared images, remote sensing images and so on. Experiment results on the fusion of different types of images have demonstrated the robustness and efficiency of the proposed method. It has been shown that the fused edge image has more complete and reliable edge information than that from any of the original image.
File Size	737225
File Format	PDF
ISBN	0780393031
DOI	10.1109/ICIA.2005.1635126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Image edge detection Image processing Infrared imaging Detectors Sensor fusion Robustness Image fusion Analysis of variance Remote sensing
Content Type	Text
Resource Type	Article

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