改进的FastICA在盲图像分离中的应用

Application of blind image separation based on improved FastICA algorithm

盲源分离(BSS)是信号处理领域的一个热点问题。独立分量分析(ICA)是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,已广泛应用于信号处理领域。为了有效地对混合图像进行盲源分离,介绍了一种基于改进的快速固定点算法(FastICA),对经过随机线性混合后的模糊图像进行盲源分离。仿真结果显示,该算法可以很有效地对线性混合图像进行盲源分离。

Blind source separation（BSS）was a hot point of signal processing. Independent component analysis （IGA） was widely used in signal processing, which is a signal analysis method based on signal＇s high order cumulants, it can lind out the latent independent components in data. This paper introduce an improved fast fixed-point independent component analysis algorithm（FastlCA）, used to separate random mixed images. As the final, a good result was obtained through emulating with MATLAB.