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Directional Total Generalized Variation Regularization for Impulse Noise Removal

  • Rasmus Dalgas KongskovEmail author
  • Yiqiu Dong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Directional total generalized variation Impulse noise Variational methods Regularization Image restoration 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Applied Mathematics and Computer Science at Technical University of DenmarkKongens LyngbyDenmark

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