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Locally adaptive total variation for removing mixed Gaussian–impulse noise

Langer, A. LU (2019) In International Journal of Computer Mathematics 96(2). p.298-316
Abstract

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.

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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
automated parameter selection, combined L^1/L^2 data fidelity, Locally dependent regularization parameter, mixed Gaussian–impulse noise, total variation minimization
in
International Journal of Computer Mathematics
volume
96
issue
2
pages
19 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85042410852
ISSN
0020-7160
DOI
10.1080/00207160.2018.1438603
language
English
LU publication?
no
additional info
Publisher Copyright: © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.
id
01cd6498-ba77-4514-9212-2b4cb916e306
date added to LUP
2021-03-15 22:26:10
date last changed
2022-04-19 05:01:48
@article{01cd6498-ba77-4514-9212-2b4cb916e306,
  abstract     = {{<p>The minimization of a functional consisting of a combined L<sup>1</sup>/L<sup>2</sup> 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.</p>}},
  author       = {{Langer, A.}},
  issn         = {{0020-7160}},
  keywords     = {{automated parameter selection; combined L^1/L^2 data fidelity; Locally dependent regularization parameter; mixed Gaussian–impulse noise; total variation minimization}},
  language     = {{eng}},
  month        = {{02}},
  number       = {{2}},
  pages        = {{298--316}},
  publisher    = {{Taylor & Francis}},
  series       = {{International Journal of Computer Mathematics}},
  title        = {{Locally adaptive total variation for removing mixed Gaussian–impulse noise}},
  url          = {{http://dx.doi.org/10.1080/00207160.2018.1438603}},
  doi          = {{10.1080/00207160.2018.1438603}},
  volume       = {{96}},
  year         = {{2019}},
}