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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 49–60Cite as

Color Image Deblurring with Impulsive Noise

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

Abstract

We propose a variational approach for deblurring and impulsive noise removal in multi-channel images. A robust data fidelity measure and edge preserving regularization are employed. We consider several regularization approaches, such as Beltrami flow, Mumford-Shah and Total-Variation Mumford-Shah. The latter two methods are extended to multi-channel images and reformulated using the Γ-convergence approximation. Our main contribution is in the unification of image deblurring and impulse noise removal in a multi-channel variational framework. Theoretical and experimental results show that the Mumford-Shah and Total Variation Mumford Shah regularization methods are superior to other color image restoration regularizers. In addition, these two methods yield a denoised edge map of the image.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, Tel-Aviv University, Israel

    Leah Bar & Nahum Kiryati

  2. Dept. of Mathematics, Technion, Israel

    Alexander Brook

  3. Dept. of Applied Mathematics, Tel-Aviv University, Israel

    Nir Sochen

Authors
  1. Leah Bar
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  2. Alexander Brook
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  3. Nir Sochen
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  4. Nahum Kiryati
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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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Bar, L., Brook, A., Sochen, N., Kiryati, N. (2005). Color Image Deblurring with Impulsive Noise. 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_5

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  • DOI: https://doi.org/10.1007/11567646_5

  • 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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