Blind Source Separation with Wavelet Based ICA Technique Using Kurtosis

Abstract

This paper deals with the problem of blind separation of digital images from mixtures. It proposes a wavelet -based Independent Component Analysis (ICA) method using Kurtosis for blind image source separation. In this method, the observations are transformed into an adequate representation using wavelet packet decomposition and a Kurtosis criterion. The simulation results of performance measures show a considerable improvement when compared to the FastICA. The Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Segmental Signal-to-Noise Ratio (SNRseg) are used to evaluate the quality of the separated images.