Blind source separation in noisy mixtures based on curvelet transform and independent component analysis
基于curvelet变换和独立分量分析的含噪盲源分离

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.