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Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing

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Neural Information Processing (ICONIP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4985))

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Abstract

Nonnegative Matrix Factorization (NMF) has already found many applications in image processing and data analysis, including classification, clustering, feature extraction, pattern recognition, and blind image separation. In the paper, we extend the selected NMF algorithms by taking into account local smoothness properties of source images. Our modifications are related with incorporation of the Gibbs prior, which is well-known in many tomographic image reconstruction applications, to a underlying blind image separation model. The numerical results demonstrate the improved performance of the proposed methods in comparison to the standard NMF algorithms.

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Authors and Affiliations

  1. RIKEN Brain Science Institute, Wako-shi, Saitama, Japan

    Rafal Zdunek & Andrzej Cichocki

Authors
  1. Rafal Zdunek
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  2. Andrzej Cichocki
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Masumi Ishikawa Kenji Doya Hiroyuki Miyamoto Takeshi Yamakawa

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© 2008 Springer-Verlag Berlin Heidelberg

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Zdunek, R., Cichocki, A. (2008). Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing. In: Ishikawa, M., Doya, K., Miyamoto, H., Yamakawa, T. (eds) Neural Information Processing. ICONIP 2007. Lecture Notes in Computer Science, vol 4985. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69162-4_54

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  • DOI: https://doi.org/10.1007/978-3-540-69162-4_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69159-4

  • Online ISBN: 978-3-540-69162-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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