基于curvelet变换和独立分量分析的含噪盲源分离

独立分量分析（ICA）是基于信号高阶统计量的盲源分离方法，在高阶统计量方法中，由于高斯信号的高阶累计量为零，所以系统存在加性高斯噪声时就难以处理。提出了一种基于curvelet阈值去噪和FastICA算法的含噪信号盲分离的方法，并对高斯噪声环境下的混合图像进行了盲分离的仿真。结果表明，该方法能很好地解决由于存在加性高斯噪声而导致经典ICA算法性能发生严重恶化的问题；同时将curvelet变换去噪应用于含噪图像的盲源分离中，可以提高混合图像的信噪比，相对于小波去噪后的ICA算法，其分离性能有很大改善。

Blind source separation in noisy mixtures based on curvelet transform and independent component analysis

Independent Component Analysis (ICA) is a method for blind source separation based on higher-order statistics. It is hard to deal with the signal in the environment of Gaussian noise, because the higher-order cumulant of Gaussian signal is zero. A noisy image separation algorithm based on Curvelet threshold de-noising processing and FastICA was proposed. The results of simulation in Gaussian noise show that it can solve the problem of performance deterioration of ICA algorithms while processing noisy mixtures. Curvelet transform used in noisy images separation can improve the quality of Signal-to Noise Ratio (SNR) and the performance of separation compared with ICA that has been de-noised by wavelet.