Locally adaptive total variation for removing mixed Gaussian–impulse noise

A. Langer

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
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The minimization of a functional consisting of a combined L1/L2 data fidelity term and a total variation regularization term with a locally varying regularization parameter for the removal of mixed Gaussian–impulse noise is considered. Based on a related locally constrained optimization problem, algorithms for automatically selecting the spatially varying parameter are presented. Numerical experiments for image denoising are shown, which demonstrate that the locally varying parameter selection algorithms are able to generate solutions which are of higher restoration quality than solutions obtained with scalar parameters.
Original languageEnglish
Pages (from-to)298-316
Number of pages19
JournalInternational Journal of Computer Mathematics
Issue number2
Early online date23 Feb 2018
Publication statusPublished - 2019
Externally publishedYes

Bibliographical note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Computer Mathematics on 23/02/2018, available online: http://www.tandfonline.com/10.1080/00207160.2018.1438603

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.


  • Locally dependent regularization parameter
  • automated parameter selection
  • mixed Gaussian–impulse noise
  • combined L1/L2 data fidelity
  • total variation minimization

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