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Blind Image Separation with Neural Learning Based on Information Theory and Higher-order Statistics  

Cho, Hyun-Cheol (동아대학교 전기공학과)
Lee, Kwon-Soon (동아대학교 전기공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.57, no.8, 2008 , pp. 1454-1463 More about this Journal
Abstract
Blind source separation by independent component analysis (ICA) has applied in signal processing, telecommunication, and image processing to recover unknown original source signals from mutually independent observation signals. Neural networks are learned to estimate the original signals by unsupervised learning algorithm. Because the outputs of the neural networks which yield original source signals are mutually independent, then mutual information is zero. This is equivalent to minimizing the Kullback-Leibler convergence between probability density function and the corresponding factorial distribution of the output in neural networks. In this paper, we present a learning algorithm using information theory and higher order statistics to solve problem of blind source separation. For computer simulation two deterministic signals and a Gaussian noise are used as original source signals. We also test the proposed algorithm by applying it to several discrete images.
Keywords
Blind image separation; ICA; Neural network; Information theory; Higher-order statistics;
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