独立分量分析在有噪图像分离中的应用

Application of Independent Component Analysis on Noisy Image Separation

独立分量分析(independentcomponentanalysis,ICA)是基于信号高阶统计量的盲源分离方法。在分析独立分量分析的基本模型及方法的基础上,讨论了有噪信号的独立分量分析(NoisyICA),利用小波阈值去噪和FastICA算法进行了有噪混合图像分离的仿真研究。结果表明,对于含有加性观测噪声的混合图像的分离,先去噪处理再进行独立分量分离的效果要优于独立分量分离后再去噪的效果。

Independent Component Analysis(ICA) is a novel method for blind source separation based on high statistics. The basic model and methods of ICA are introduced, and then the ICA of noisy signals is discussed. The method of wavelet threshold de noising and the algorithm of Fast ICA are studied with the simulation of noisy mixed image separation. The results show that for the mixed images with additive white Gaussian noise, it’s better to de noise the images before applying ICA than to apply ICA first and then de noise the independent components.