Blind Separation of Noisy Mixed Images Based on Neural Network and Independent Component Analysis

Abstract

Blind source separation problem has 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 contribution, the covariance of noise was foregone, a noisy multiple channels blind source separation algorithm was proposed based on neural network and independent component. At prewhitening, the data have no noise was used to whiten the noisy data, and the windage wipe off technique was used to correct the infection of noise, a neural network model having denoise capability was adopted to realize the multiple channels blind source separation method for mixing images corrupter with white noise. 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.