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Proceedings Paper

Multisensor image fusion using fast discrete curvelet transform
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Paper Abstract

This paper describes a novel approach to multisensor image fusion using a new mathematical transform: the curvelet transform. The transform has shown promising results over wavelet transform for 2-D signals. Wavelets, though well suited to point singularities have limitation with orientation selectivity, and therefore, do not represent two-dimensional singularities (e.g. smooth curves) effectively. Curvelet improves wavelet by incorporating a directional component. This paper employs the curvelet transform for image fusion. Based on the local energy of direction curvelet subbands, we give the definition of local band-limited contrast and use it as one of the fusion rules. The local band-limited contrast can reflect the response of local image features in human visual system truly. When used to image fusion in noiseless circumstance, it is effective. But in noisy circumstance, it is not always robust. According to the different characteristics between image features and noise, the local directional energy entropy is proposed. It can distinguish the noise and local image features. In this paper, the combination of local band-limited contrast and local directional energy entropy is used as image fusion. Experimental results show that it is robust in noisy and noiseless image fusion system.

Paper Details

Date Published: 14 November 2007
PDF: 9 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679004 (14 November 2007); doi: 10.1117/12.747921
Show Author Affiliations
Chengzhi Deng, Huazhong Univ. of Science and Technology (China)
Hanqiang Cao, Huazhong Univ. of Science and Technology (China)
Chao Cao, Shanghai Jiao Tong Univ. (China)
Shengqian Wang, Jiangxi Science and Technology Normal Univ. (China)


Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications

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