基于QPSO和ICA的图像盲分离方法研究  

Research of Image Blind Separation Method Based on QPSO and ICA

作　　者：范文兵[1] 邢军阳[1] 李海涛[1] 代琳娜[1] 

机构地区：[1]郑州大学信息工程学院,河南郑州450001

出　　处：《郑州大学学报（工学版）》2012年第3期106-109,112,共5页Journal of Zhengzhou University（Engineering Science）

基　　金：河南省重点科技攻关项目(112102310073);河南省教育厅自然科学研究计划项目(2009A520028)

摘　　要：针对ICA技术中常用的普通梯度算法容易陷入局部最优,提出了一种基于量子行为的粒子群算法和独立分量分析相结合的盲源分离新算法.以负熵作为独立分量分析的目标函数,用QPSO算法代替普通梯度算法,对瞬时混合信号进行分离,给出了算法的具体步骤.实验结果表明,该算法能够有效实现图像的盲源分离.同时与其他算法对比,体现了该算法更高的性能.In this paper, we introduce the Independent Component Analysis （ICA） and Quantum Particle Swarm Optimization （PSO） briefly. As the ordinary gradient algorithm of ICA technology is easy to fall into local optimum, we proposed quantum-behavior based particle swarm optimization and independent component analysis for blind source separation combining new algorithms. This algorithm takes negative entropy as the objective function of independent component analysis, replaces the ordinary gradient algorithm with QPSO algorithm and separates the instantaneous mixed signals, All the steps of this algorithm are given in this paper. Experiment is show that the proposed algorithm can effectively achieve the image of the blind source separation. Compared with other algorithms, this algorithm shows better performance.

关 键 词：独立分量分析 量子粒子群 盲源分离 负熵 

分 类 号：TN911.7[电子电信—通信与信息系统]