摘要
独立分量分析是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,近年来作为信号处理和图像处理领域的强有力的分析处理工具得到广泛的关注和研究。在介绍了独立分量分析的基本概念和各种实现算法及其性能的基础上,综述了独立分量分析在图像处理上的应用,最后结合作者的研究探索,总结了独立分量分析的研究新进展和发展趋势。
Independent Component Analysis（ICA） is a signal analysis method based on signal＇s high order cumulants,it can find out the latent independent components in data.With it＇s widely applications in signal and image processing,many researches have focus on ICA recently.In this paper,the authors briefly review the theory of independent component analysis and its applications in image processing.After a general introduction to the definitions and models of ICA, the authors discuss in more detail the contrast functions and optimization algorithms of ICA.Then review various applications of ICA in image processing.In the later part of this paper,the future works and the research directions of ICA are reviewed combining to the works of the author.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第23期172-177,共6页
Computer Engineering and Applications
基金
国家部委科技重点实验室基金资助项目
关键词
独立分量分析
图像处理
盲分离
Independent Component Analysis （ICA）
image processing
blind source separation
