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An Image Separation Scheme using Independent Component Analysis and Expectation-Maximization  

오범진 (국립한밭대학교 전자공학과)
김성수 (충북대학교 전기공학과)
유정웅 (충북대학교 전기공학과)
Publication Information
Journal of KIISE:Information Networking / v.30, no.1, 2003 , pp. 24-29 More about this Journal
Abstract
In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing schemes, which represents the information from observations as a set of random variables in the from of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other. which can be utilized in linear projection.. However, it has been known that ICA does not establish good performance in source separation by itself. Thus, in order to overcome this limitation, there have been many techniques that are designed to reinforce the good properties of ICA, which improves the mixed image separation. Unfortunately, the innovation process still needs to be studied since it yields inconsistent innovation process that is attached to the ICA, the expectation and maximization process is added. The results presented in this paper show that the proposed improves the image separation as presented in experiments.
Keywords
Mixed image Separation; Independent Component Analysis; Innovation Process Expectation and Maximization;
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