Non-Overlapping Domain Decomposition Methods For Dual Total Variation Based Image Denoising
(2014) In Journal of Scientific Computing 62(2). p.456-481- Abstract
In this paper non-overlapping domain decomposition methods for the pre-dual total variation minimization problem are introduced. Both parallel and sequential approaches are proposed for these methods for which convergence to a minimizer of the original problem is established. The associated subproblems are solved by a semi-smooth Newton method. Several numerical experiments are presented, which show the successful application of the sequential and parallel algorithm for image denoising.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/fcf0f2d6-361b-4bd8-9380-b73fb76225f9
- author
- Hintermüller, Michael and Langer, Andreas LU
- publishing date
- 2014-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Convergence analysis, Convex optimization, Domain decomposition, Image reconstruction, Pre-dual, Subspace correction, Total bounded variation
- in
- Journal of Scientific Computing
- volume
- 62
- issue
- 2
- pages
- 26 pages
- publisher
- Springer
- external identifiers
-
- scopus:84921066590
- ISSN
- 0885-7474
- DOI
- 10.1007/s10915-014-9863-8
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2014, Springer Science+Business Media New York. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
- id
- fcf0f2d6-361b-4bd8-9380-b73fb76225f9
- date added to LUP
- 2021-03-15 22:32:13
- date last changed
- 2022-02-23 15:31:46
@article{fcf0f2d6-361b-4bd8-9380-b73fb76225f9, abstract = {{<p>In this paper non-overlapping domain decomposition methods for the pre-dual total variation minimization problem are introduced. Both parallel and sequential approaches are proposed for these methods for which convergence to a minimizer of the original problem is established. The associated subproblems are solved by a semi-smooth Newton method. Several numerical experiments are presented, which show the successful application of the sequential and parallel algorithm for image denoising.</p>}}, author = {{Hintermüller, Michael and Langer, Andreas}}, issn = {{0885-7474}}, keywords = {{Convergence analysis; Convex optimization; Domain decomposition; Image reconstruction; Pre-dual; Subspace correction; Total bounded variation}}, language = {{eng}}, number = {{2}}, pages = {{456--481}}, publisher = {{Springer}}, series = {{Journal of Scientific Computing}}, title = {{Non-Overlapping Domain Decomposition Methods For Dual Total Variation Based Image Denoising}}, url = {{http://dx.doi.org/10.1007/s10915-014-9863-8}}, doi = {{10.1007/s10915-014-9863-8}}, volume = {{62}}, year = {{2014}}, }