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Filtering-Free Blind Separation of Correlated Images

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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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.

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© 2005 Springer-Verlag Berlin Heidelberg

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Vrins, F., Lee, J.A., Verleysen, M. (2005). Filtering-Free Blind Separation of Correlated Images. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_134

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  • DOI: https://doi.org/10.1007/11494669_134

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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