Filtering-Free blind separation of correlated images

Authors:
Frédéric Vrins;John A. Lee;Michel Verleysen
Affiliations:
Machine Learning Group, Université catholique de Louvain, Louvain-la-Neuve, Belgium;Machine Learning Group, Université catholique de Louvain, Louvain-la-Neuve, Belgium;Machine Learning Group, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 2
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Abstract

When using ICA for image separation, a well-known problem is that most often a large correlation exists between the sources. Because of this dependence, there is no more guarantee that the global maximum of the ICA contrast matches the outputs to the sources. In order to overcome this problem, some preprocessing can be used, like e.g. band-pass filtering. However, those processings involve parameters, for which the optimal values could be tedious to adjust. In this paper, it is shown that a simple ICA algorithm can recover the sources, without any other preprocessing than whitening, when they are correlated in a specific way. First, a single source is extracted, and next, a parameter-free postprocessing is applied for optimizing the extraction of the remaining sources.