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International Workshop on Variational, Geometric, and Level Set Methods in Computer Vision

VLSM 2005: Variational, Geometric, and Level Set Methods in Computer Vision pp 73–84Cite as

Image Cartoon-Texture Decomposition and Feature Selection Using the Total Variation Regularized L 1 Functional

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3752))

Abstract

This paper studies the model of minimizing total variation with an L 1-norm fidelity term for decomposing a real image into the sum of cartoon and texture. This model is also analyzed and shown to be able to select features of an image according to their scales.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, USA

    Wotao Yin & Donald Goldfarb

  2. Department of Mathematics, University of California at Los Angeles, Los Angeles, CA, USA

    Stanley Osher

Authors
  1. Wotao Yin
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  2. Donald Goldfarb
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  3. Stanley Osher
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Editor information

Editors and Affiliations

  1. MAS - Ecole Centrale Paris, Grande Voie des Vignes, 92295, Chatenay-Malabry, France

    Nikos Paragios

  2. I.N.R.I.A, 2004 route des lucioles,, 06902, Sophia-Antipolis, France

    Olivier Faugeras

  3. Department of Mathematics, UCLA,  

    Tony Chan

  4. Image and Pattern Analysis Group, Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

    Christoph Schnörr

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Yin, W., Goldfarb, D., Osher, S. (2005). Image Cartoon-Texture Decomposition and Feature Selection Using the Total Variation Regularized L 1 Functional. In: Paragios, N., Faugeras, O., Chan, T., Schnörr, C. (eds) Variational, Geometric, and Level Set Methods in Computer Vision. VLSM 2005. Lecture Notes in Computer Science, vol 3752. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11567646_7

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  • DOI: https://doi.org/10.1007/11567646_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29348-4

  • Online ISBN: 978-3-540-32109-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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