摘要
提出一种将非负矩阵分解思想用于图像融合的算法.在非负矩阵分解过程中,适当地选取特征空间的维数可以获取原始数据的局部特征.首先分析了使用非负矩阵分解算法提取图像综合特征的原理,并给出了一个可视化实例;将参与融合的图像作为原始数据,特征空间的维数选为1,利用非负矩阵分解得到的特征基包含了原始图像的整体特征,这个特征基图像就是原始图像的融合结果.多类不同模态图像融合的实验结果表明,文中算法比小波变换的方法具有更好的融合效果.
The theory of NMF（Non-negative Matrix Factorization） technique is investigated and a novel method based on NMF is presented for image fusion in this paper. It is shown that the local feature could be obtained by choosing suitable dimension of the feature subspace in NMF. In this paper, the principle of extracting the global feature of an image using NMF is analyzed and a visual example is given. We point out that when using NMF, if the dimension of the feature subspace is set to 1, the resultant feature base is just the fusion result of the original input images and the feature base contains the global feature of the original images. Experiments show that the method proposed in the paper performs better in preserving the feature information for the test images than wavelet transform does.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2005年第9期2029-2032,共4页
Journal of Computer-Aided Design & Computer Graphics
基金
国防科技预研基金
关键词
非负矩阵分解
图像融合
特征基
全局特征
non-negative matrix factorization
image fusion
feature base
global feature
