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Total Variation Minimization and a Class of Binary MRF Models

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3757))

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Abstract

We observe that there is a strong connection between a whole class of simple binary MRF energies and the Rudin-Osher-Fatemi (ROF) Total Variation minimization approach to image denoising. We show, more precisely, that solutions to binary MRFs can be found by minimizing an appropriate ROF problem, and vice-versa. This leads to new algorithms. We then compare the efficiency of various algorithms.

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Author information

Authors and Affiliations

  1. CMAP (CNRS UMR 7641), Ecole Polytechnique, 91128, Palaiseau Cedex, France

    Antonin Chambolle

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  1. Antonin Chambolle
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Editor information

Editors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

  2. University of Florida, 32611, Gainesville, FL, USA

    Baba Vemuri

  3. Department of Statistics, University of California, Los Angeles

    Alan L. Yuille

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© 2005 Springer-Verlag Berlin Heidelberg

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Chambolle, A. (2005). Total Variation Minimization and a Class of Binary MRF Models. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_10

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  • DOI: https://doi.org/10.1007/11585978_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30287-2

  • Online ISBN: 978-3-540-32098-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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