A Neural Network Algorithm based Blind source Separation using Fast Fixed Point Independent Component Analysis

Image separation is defined as decomposing a real world image mixture into individual images objects. Independent component analysis is an active area of research and is being utilized for its capability in statistically independent separation images. Neural network algorithm ICA has been used to extract interference and mixed images and a very rapid developed statistical method during last few years, but because of very less literature (content) is available on the performance and analysis, i.e., how does it behave in communication science and environment? So, in this paper fast fixed point algorithms for ICA-based blind source separation has been presented. In blind source separation primary goal is to recover all original images using the observed mixtures only. Independent Component Analysis (ICA) is based on higher order statistics aiming at searching for the components in the mixed signals that are statistically as independent from each other as possible. In this paper a neural network algorithm based blind source separation using fast fixed point Independent component analysis simulations is presented to demonstrate the results of our analysis. Keywords— Fixed point ICA algorithem ,Mixer signal , Blind source separation ,Peak signal to noise ratio (PSNR).