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基于多特征模糊聚类的图像融合方法
引用本文:苏冬雪,吴小俊.基于多特征模糊聚类的图像融合方法[J].计算机辅助设计与图形学学报,2006,18(6):838-843.
作者姓名:苏冬雪  吴小俊
作者单位:1. 江苏科技大学电子信息学院,镇江,212003
2. 江苏科技大学电子信息学院,镇江,212003;中国科学院沈阳自动化研究所机器人学重点实验室,沈阳,110015
基金项目:中国科学院资助项目;中国科学院重点实验室基金;江苏省自然科学基金;江苏省图像处理与图像通信实验室基金
摘    要:首先利用模糊C-均值聚类算法在多特征形成的特征空间上对图像进行区域分割,并在此基础上对区域进行多尺度小波分解;然后利用柯西函数构造区域的模糊相似度,应用模糊相似度及区域信息量构造加权因子,从而得到融合图像的小波系数;最后利用小波逆变换得到融合图像.采用均方根误差、峰值信噪比、熵、交叉熵和互信息5种准则评价融合算法的性能.实验结果表明,文中方法具有良好的融合特性.

关 键 词:模糊聚类  柯西函数  图像融合  多尺度小波分解
收稿时间:2005-06-14
修稿时间:2005-09-16

Image Fusion Based on Multi-feature Fuzzy Clustering
Su Dongxue,Wu Xiaojun.Image Fusion Based on Multi-feature Fuzzy Clustering[J].Journal of Computer-Aided Design & Computer Graphics,2006,18(6):838-843.
Authors:Su Dongxue  Wu Xiaojun
Affiliation:School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003;Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110015
Abstract:In this method, the fuzzy C-means clustering algorithm is used to segment the image in the feature space formed by multiple features of training samples, and then a multi-scale wavelet decomposition is performed on each region. Second, the weighting factors are constructed based on the local energy and the fuzzy similarity measure defined by Cauchy function. The wavelet coefficients of the fused image are acquired by the weighting factors. Finally, the fused image is obtained by taking the inverse wavelet transform. The performance of the image fusion method is evaluated using five criteria including root mean square error, peek-to-peek signal-to-noise ratio, entropy, cross entropy and mutual information. The evaluation results validate the proposed image fusion method.
Keywords:fuzzy clustering  Cauchy function  image fusion  multi-scale wavelet decomposition
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