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Quaternion-Based Color Image Smoothing Using a Spatially Varying Kernel

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Book cover Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5681))

Abstract

Addressing the issue of feature/detail preserving color image smoothing, we propose a novel unified approach based on a quaternion framework. The main idea is to holistically extract the local orientation information at each lattice point, and then to incorporate it into the smoothing process. We introduce a new Quaternion Gabor Filter to derive the local orientation information in color images. This derived orientation information is modeled using a continuous mixture of appropriate exponential basis functions. We solve the continuous mixture integral in analytic form, and develop a spatially varying kernel which respects to the local geometry at each lattice point in a color image. Superior performance of our smoothing framework is demonstrated via comparison to competing state-of-the-art algorithms in literature.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

    Özlem N. Subakan & Baba C. Vemuri

Authors
  1. Özlem N. Subakan
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  2. Baba C. Vemuri
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Editor information

Editors and Affiliations

  1. Institut für Informatik III, Rö, Universität Bonn, merstraße 164, 53117, Bonn, Germany

    Daniel Cremers

  2. Computer Science Department, University of Western Ontario, N6A 5A5, London, ON, Canada

    Yuri Boykov

  3. Microsoft Research, J.J. Thomson Avenue, CB3 OFB, Cambridge, United Kingdom

    Andrew Blake

  4. Institut für Informatik III, Universität Bonn, Römerstraße 164, 53117, Bonn, Germany

    Frank R. Schmidt

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© 2009 Springer-Verlag Berlin Heidelberg

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Subakan, Ö.N., Vemuri, B.C. (2009). Quaternion-Based Color Image Smoothing Using a Spatially Varying Kernel. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_31

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  • DOI: https://doi.org/10.1007/978-3-642-03641-5_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03640-8

  • Online ISBN: 978-3-642-03641-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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