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Proceedings Paper

Multiwavelet based multispectral image fusion for corona detection
Author(s): Xin Wang; Feng Yan; Yong-xin Sui; Huai-jiang Yang; Yun-jie Pang
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Paper Abstract

Image fusion refers to the integration of complementary information provided by various sensors such that the new images are more useful for human or machine perception. Multiwavelet transform has simultaneous orthogonality, symmetry, compact support, and vanishing moment, which are not possible with scalar wavelet transform. Multiwavelet analysis can offer more precise image analysis than wavelet multiresolution analysis. In this paper, a new image fusion algorithm based on discrete multiwavelet transform (DMWT) to fuse the dual-spectral images generated from the corona detection system is presented. The dual-spectrum detection system is used to detect the corona and indicate its exact location. The system combines a solar-blind UV ICCD with a visible camera, where the UV image is useful for detecting UV emission from corona and the visible image shows the position of the corona. The developed fusion algorithm is proposed considering the feature of the UV and visible images adequately. The source images are performed at the pixel level. First, a decomposition step is taken with the DMWT. After the decomposition step, a pyramid for each source image in each level can be obtained. Then, an optimized coefficient fusion rule consisting of activity level measurement, coefficient combining and consistency verification is used to acquire the fused coefficients. This process reduces the impulse noise of UV image. Finally, a new fused image is obtained by reconstructing the fused coefficients using inverse DMWT. This image fusion algorithm has been applied to process the multispectral UV/visible images. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach.

Paper Details

Date Published: 20 January 2006
PDF: 6 pages
Proc. SPIE 6027, ICO20: Optical Information Processing, 60272R (20 January 2006); doi: 10.1117/12.668299
Show Author Affiliations
Xin Wang, Jilin Univ. (China)
Feng Yan, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Yong-xin Sui, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Huai-jiang Yang, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Yun-jie Pang, Jilin Univ. (China)


Published in SPIE Proceedings Vol. 6027:
ICO20: Optical Information Processing

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