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Spatio-Temporal Scale Selection in Video Data Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Directional Total Generalized Variation Regularization for Impulse Noise Removal

Authors : Rasmus Dalgas Kongskov, Yiqiu Dong

Published in: Scale Space and Variational Methods in Computer Vision

Publisher: Springer International Publishing

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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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previous chapter Below the Surface of the Non-local Bayesian Image Denoising Method
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Metadata
Title
Directional Total Generalized Variation Regularization for Impulse Noise Removal
Authors
Rasmus Dalgas Kongskov
Yiqiu Dong
Copyright Year
2017
Book
Scale Space and Variational Methods in Computer Vision

Print ISBN: 978-3-319-58770-7

Electronic ISBN: 978-3-319-58771-4

Copyright Year: 2017

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

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