A Neural Learning Algorithm of Blind Separation of Noisy Mixed Images Based on 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 proposed approaches to independent component analysis when the measured signals are contaminated by additive noise. A noisy multiple channels neural learning algorithm of blind separation is proposed based on independent component analysis. The data have no noise are used to whiten the noisy data, and the windage wipe off technique is used to correct the infection of noise, a neural network model having denoise capability is adopted to recover some original signals from their noisy mixtures observed by the same number of sensors. And a relaxation factor is introduced into the iteration algorithm, thus the new algorithm can implement convergence. Computer simulations and experiment results prove the feasibility and validity of the neural network modeling and control method based on independent component analysis, which can renew the original images effectively.