基于快速核独立分量分析的图像反光分离研究  

Separating Reflections from Image Using Fast Kernel Independent Component Analysis

作　　者：刘景波[1] 万小磊[2] 金炜东[1] 

机构地区：[1]西南交通大学电气工程学院,四川成都610031 [2]南阳理工学院科研处,河南南阳473000

出　　处：《激光与光电子学进展》2010年第5期34-38,共5页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(60572143;60971103)资助课题

摘　　要：透过玻璃对场景成像,图像由场景实像和反光虚像线性叠加而成,直接影响到图像分析处理和计算机视觉应用等,可采用盲源分离方法进行反光分离。图像成像过程中的非线性因素,会使线性独立分量分析(ICA)用于反光分离结果不够精确。采用基于Hilbert-Schmidt独立性判断准则的快速核独立分量分析(FastKICA)进行图像反光分离。实验表明,快速核独立分量分析用于图像反光分离,在分离精度和速度上优于线性独立分量分析和常规核独立分量分析(KICA)。When imaging a scene through a glass, the image is a linear superposition of a real image observed through a glass and a virtual image reflected on it. This brings adverse effect on image processing, computer vision, etc. Reflective separation can be done by the original method of blind separation, nonlinearity in the course of imaging will degrade precision of separating reflections using linear independent component analysis （ICA）. Kernel independent component analysis （KICA） can effectively deal with the nonlinearity. The reflections is removed by using FastKICA based on the Hilbert-Schmidt independence criterion. Experiments show that FastKICA is more effective than linear independent component analysis and conventional KICA.

关 键 词：图像处理 反光分离 快速核独立分量分析 非线性 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]