Research of Blind Image Separation Based on Independent Component Analysis

被引：0
|
作者：
Tian, Qi-chong [1 ]
Zheng, Wei-guo [1 ]
Feng, Shao-huai [1 ]
Zhang, Li-dong
机构：
[1] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou, Peoples R China
来源：
ACC 2009: ETP/IITA WORLD CONGRESS IN APPLIED COMPUTING, COMPUTER SCIENCE, AND COMPUTER ENGINEERING | 2009年
关键词：
Blind Image Separation; ICA; JADE; FastICA; Performance index; Separation time;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Independent Component Analysis (ICA) based on the higher-order statistics of signals, can separate source signals which are both statistically independent and non-Gaussian from the mixing signals. And images can be regarded as two-dimensional signals, so it is feasible to separate mixing images using ICA. In this paper, the basic model of ICA and the principles of the JADE algorithm and the FastICA algorithm are introduced, and thus got some valuable experimental results by separating a same group of mixing images using JADE and FastICA. Based on these results, it comes to the conclusion that the FastICA algorithm is superior to the JADE algorithms in the performance index and separation time.
引用
收藏
页码：430 / 433
页数：4
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