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浏览/检索结果: 共1条，第1-1条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Multiwavelet based multispectral image fusion for corona detection (EI CONFERENCE) 会议论文 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Wang X.; Yan F.; Sui Y.-X.; Yang H.-J.; Pang Y.-J. 收藏  |  浏览/下载：13/0  |  提交时间：2013/03/25 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.