摘要
由于乘性噪声的存在,严重限制了标准ICA的使用。在分析独立分量分析的基本模型的基础上,讨论了有噪信号的独立分量分析(Noisy ICA)。提出一种新的基于四阶统计量的方法来消除乘性噪声,分离出独立的源信号。通过寻求噪声线性转换的统计结构,依据代价函数最小来获取解混阵B,从而分离出多维观测信号。最后把算法应用于含噪的混合图像,通过仿真显示算法很好的分离了源信号。
The existence of multiplieative noise greatly limits the applicability of independent component analysis. The basic model of ICA are introduced, and then the ICA of noisy signals is discusse. This paper proposes a method based on fourth-orderstatistic to eliminate multiplicative noise and separate out independent sources. In the paper, the statistical structure of a linear transformation of noisy data is studied, and the statistical structure is used to find the inverse of the mixing matrix by minimization of J. The method is efficient and robust by simulation.
出处
《激光与红外》
CAS
CSCD
北大核心
2009年第6期681-684,共4页
Laser & Infrared
关键词
独立分量分析
乘性噪声
统计量
盲源分离
independent component analysis
multiplicative noise
statistic
blind source separation
作者简介
张宇波，女，副教授，硕士生导师，从事电子技术及信息处理等方面研究工作。
