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·
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.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2006年第6期838-843,共6页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(60472060
60572034)
中国科学院沈阳自动化研究所机器人学重点实验室基金(RL200108)
江苏省自然科学基金(BK2002001
BK2004058)
图像处理与图像通信实验室开放基金(KJS03038)
关键词
模糊聚类
柯西函数
图像融合
多尺度小波分解
fuzzy clustering
Cauchy function
image fusion
multi-scale wavelet decomposition
