Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (30): 168-170.DOI: 10.3778/j.issn.1002-8331.2009.30.051

• 图形、图像、模式识别 • Previous Articles     Next Articles

Blind separation of non-independent images using complexity pursuit

LI Bing1,LIU Ying2,GE Zheng-kun3,GUO Wu4   

  1. 1.Military Economic Academy,Wuhan 430035,China
    2.Commanding Communication Academy,Wuhan 430010,China
    3.Artillery Academy of PLA,Hefei 230031,China
    4.ATR Lab,National University of Defense Technology,Changsha 410073,China
  • Received:2008-06-06 Revised:2008-09-22 Online:2009-10-21 Published:2009-10-21
  • Contact: LI Bing

基于复杂性寻踪的非独立图像盲分离

李 兵1,刘 颖2,葛正坤3,郭 武4   

  1. 1.军事经济学院,武汉 430035
    2.通信指挥学院,武汉 430010
    3.炮兵学院,合肥 230031
    4.国防科技大学 ATR实验室,长沙 410073
  • 通讯作者: 李 兵

Abstract: Complexity pursuit is a recently developed algorithm using the gradient descent for separating interesting components from time series.It is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and Independent Component Analysis(ICA).In this paper,a fixed-point algorithm for complexity pursuit is introduced.The fixed-point algorithm inherits the advantages of the well-known FastICA algorithm in ICA,which is very simple,converges fast,and does not need to choose any learning step sizes.When the improved algorithm is used to demix the non-independent images,the results demonstrate the efficiency of the proposed method.

Key words: complexity pursuit, time series, image separation

摘要: 复杂性寻踪是近期发展起来的一种结合非高斯性和时间相关的投影寻踪方法,目的是在多元数据中找到一个投影方向,使得数据在该方向上的投影具有最令人感兴趣的结构。它是投影寻踪方法在时间序列应用上的扩展,相似于依赖时间的信号源盲分离和独立分离分析。由相互独立的图像混合而成的混合图像的盲分离技术已经相当成熟,但对非独立混合的图像的盲分离仍然是个难题。从时间序列的复杂性寻踪出发,推导出一个复杂性寻踪的定点算法。该算法是经典的快速独立分量分析算法(FastICA)的扩展,继承了FastICA的优点,简单易行,不需要用户选择学习率,并且算法具有快速稳定收敛的性质。该算法应用到非独立图像的混合图像的盲分离时,取得了较好的分离结果。

关键词: 复杂性寻踪, 时间序列, 图像分离

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