@inproceedings{769ac6a64f014cfe8ba2344b56c38b60,
title = "Directional Total Generalized Variation Regularization for Impulse Noise Removal",
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",
author = "Kongskov, {Rasmus Dalgas} and Yiqiu Dong",
year = "2017",
doi = "10.1007/978-3-319-58771-4_18",
language = "English",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "221--231",
booktitle = "Scale Space and Variational Methods in Computer Vision",
note = "Sixth International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017 ; Conference date: 04-06-2017 Through 08-06-2017",
}