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Directional Total Generalized Variation Regularization for Impulse Noise Removal

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Scale Space and Variational Methods in Computer Vision (SSVM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

Abstract

A recently suggested regularization method, which combines directional information with total generalized variation (TGV), has been shown to be successful for restoring Gaussian noise corrupted images. We extend the use of this regularizer to impulse noise removal and demonstrate that using this regularizer for directional images is highly advantageous. In order to estimate directions in impulse noise corrupted images, which is much more challenging compared to Gaussian noise corrupted images, we introduce a new Fourier transform-based method. Numerical experiments show that this method is more robust with respect to noise and also more efficient than other direction estimation methods.

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Acknowledgments

The authors would like to thank the reviewers for their comments and suggestions, which has helped to improve this article. The work was supported by Advanced Grant 291405 from the European Research Council.

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Authors and Affiliations

  1. Applied Mathematics and Computer Science at Technical University of Denmark, Kongens Lyngby, Denmark

    Rasmus Dalgas Kongskov & Yiqiu Dong

Authors
  1. Rasmus Dalgas Kongskov
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  2. Yiqiu Dong
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Correspondence to Rasmus Dalgas Kongskov .

Editor information

Editors and Affiliations

  1. University of Copenhagen , Copenhagen, Denmark

    François Lauze

  2. Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong

  3. Technical University of Denmark , Kongens Lyngby, Denmark

    Anders Bjorholm Dahl

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Kongskov, R.D., Dong, Y. (2017). Directional Total Generalized Variation Regularization for Impulse Noise Removal. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

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