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基于邻域方差加权平均的小波图像融合
引用本文:李敏,张小英,毛捷.基于邻域方差加权平均的小波图像融合[J].国外电子测量技术,2008,27(1):5-6,23.
作者姓名:李敏  张小英  毛捷
作者单位:乐山师范学院物理与电子信息科学系,乐山,614004
摘    要:提出了一种基于邻域方差加权平均的小波图像融合方法.利用离散小波变换分别将两幅源图像进行多尺度分解,再用不同的小波系数特征指导高频分量和低频分量的小波系数的融合.低频分量采用简单的加权平均法,高频分量采用邻域方差加权平均法,最后根据融合图像的各小波系数重构融合图像.实验表明不论从主观感受,还是采用信息熵和交叉熵作为评价标准,该方法都优于像素级融合方法.

关 键 词:图像融合  小波变换  邻域方差  融合规则

Neighboring region variance weighted mean image fusion based on wavelet transform
Li Min,Zhang Xiaoying,Mao Jie.Neighboring region variance weighted mean image fusion based on wavelet transform[J].Foreign Electronic Measurement Technology,2008,27(1):5-6,23.
Authors:Li Min  Zhang Xiaoying  Mao Jie
Abstract:This paper presents a neighborhood variance based on the weighted average of wavelet image fusion method. In the fusion scheme, the two source images are first decomposed into several components with different resolutions and directions using the discrete wavelet transform. Then the wavelet coefficients of the fused image can be obtained by using different rules. The approximate coefficient is based on simple plus mean method, and the particular coefficient is based on the neighboring region variance. Finally, the fused image is reconstructed by performing inverse discrete wavelet transform. Experiments show that regardless of the subjective feelings, or the use of information entropy and cross-entropy as an evaluation criterion, the method is better than the pixel-level fusion method.
Keywords:image fusion  wavelet transform  neighboring region variance  fusion rule
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