Directional Total Generalized Variation Regularization for Impulse Noise Removal

Rasmus Dalgas Kongskov, Yiqiu Dong

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationScale Space and Variational Methods in Computer Vision
PublisherSpringer
Publication date2017
Pages221-231
DOIs
Publication statusPublished - 2017
EventSixth International Conference on Scale Space and Variational Methods in Computer Vision - Hotel Koldingfjord, Kolding, Denmark
Duration: 4 Jun 20178 Jun 2017

Conference

ConferenceSixth International Conference on Scale Space and Variational Methods in Computer Vision
LocationHotel Koldingfjord
CountryDenmark
CityKolding
Period04/06/201708/06/2017
SeriesLecture Notes in Computer Science
Volume10302
ISSN0302-9743

Keywords

  • Directional total generalized variation
  • Impulse noise
  • Variational methods
  • Regularization
  • Image restoration

Cite this

Kongskov, R. D., & Dong, Y. (2017). Directional Total Generalized Variation Regularization for Impulse Noise Removal. In Scale Space and Variational Methods in Computer Vision (pp. 221-231). Springer. Lecture Notes in Computer Science, Vol.. 10302 https://doi.org/10.1007/978-3-319-58771-4_18