Abstract
Blind source separation problem have recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the additive noise is negligible so that it can be omitted from the consideration. To be applicable in realistic scenarios, blind source separation approaches should deal evenly with the presence of noise. In this paper, a method is proposed of combining wavelet threshold de-noising and independent component analysis to the blind source separation problem for mixing images corrupter with white noise. We first use wavelet threshold to de-noise and then use a new blind separation algorithm of FASTICA to separate the wavelet de-noised images. The result shows that this method may reduce the affect of noise and improve the signal-noise ratio (SNR) of separation images, accordingly renew the original images.
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