Blind Separation of Noisy Mixed Images Based on Wiener Filtering and Independent Component Analysis

Blind source separation problem has recently received a great deal of attention in signal processing and 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 contribution, we propose approaches to blind signal separation by wiener filtering and independent component analysis (ICA) when the measured signals are contaminated by additive noise. We first use wiener filtering to de-noise and then use the FASTICA algorithm to separate the 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.