Locally adaptive total variation for removing mixed Gaussian–impulse noise
(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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/01cd6498-ba77-4514-9212-2b4cb916e306
- author
- Langer, A. LU
- publishing date
- 2019-02-01
- 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}}, }