改进的独立分量分析算法的图像分离技术研究

独立分量分析（ICA）是在盲源分离（BSS）的研究过程中出现的一种全新信号处理和数据分析方法,该方法基于信号的高阶统计量,在图像的处理中起着越来越重要的作用.文章主要将改进的ICA优秀算法优化,并运用于多通道混合图像分离与处理中,通过与基于特征分析的线性变换技术的对比,以及探讨噪声对混合图像的ICA分离的影响,取得了一些有价值的仿真实验结果.从实验结论可以看出,不同ICA算法在混合图像分离应用中的优势得以充分体现,分离有噪图像方面存在的缺陷以及改进方法同样值得关注.

Research of image separation based on improved independent component analysis

Independent Component Analysis(ICA) is a brand new way of blind source separation(BSS) in signal-processing and data-analysis during research,which is based on the higher-order statistics information of source.It plays an important part in image processing and becomes more and more essential.Some improved advance ICA algorithms were implemented in our experiment of image separation.Then we discussed the influence of noise added to the mixed images,and got some useful data and results about ICA in image separation experiment through the contrast to the algorithms based on linearly transformed technique of FA(Factor Analysis).From the conclusion of the experiment,the advantages of ICA were vividly showed to us,as well as the shortages as dealing with mixed images with added noise.Thus it need be paid more attention by us.