Studies on independent component analysis for watermarking and nonlinear blind source separation

Abstract

Independent Component Analysis (ICA) is one of the important methods in statistics and signal processing that deals with the data representation (transformation) problem. From the observed data, the goal of ICA is to estimate the mixing system and the underlying components that create those observed data. The most interesting thing is that ICA does it 'blindly' without prior knowledge of either the sources or the mixing system. Due to these attractive characteristics, ICA has been applied in many fields of science and engineering, for example, noise reduction, biomedicine, audio systems, telecommunication, and many others.