Automated Parameter Selection for Total Variation Minimization in Image Restoration
(2017) In Journal of Mathematical Imaging and Vision 57(2). p.239-268- Abstract
Algorithms for automatically selecting a scalar or locally varying regularization parameter for total variation models with an Lτ-data fidelity term, τ∈ { 1 , 2 } , are presented. The automated selection of the regularization parameter is based on the discrepancy principle, whereby in each iteration a total variation model has to be minimized. In the case of a locally varying parameter, this amounts to solve a multiscale total variation minimization problem. For solving the constituted multiscale total variation model, convergent first- and second-order methods are introduced and analyzed. Numerical experiments for image denoising and image deblurring show the efficiency, the competitiveness, and the performance of the... (More)
Algorithms for automatically selecting a scalar or locally varying regularization parameter for total variation models with an Lτ-data fidelity term, τ∈ { 1 , 2 } , are presented. The automated selection of the regularization parameter is based on the discrepancy principle, whereby in each iteration a total variation model has to be minimized. In the case of a locally varying parameter, this amounts to solve a multiscale total variation minimization problem. For solving the constituted multiscale total variation model, convergent first- and second-order methods are introduced and analyzed. Numerical experiments for image denoising and image deblurring show the efficiency, the competitiveness, and the performance of the proposed fully automated scalar and locally varying parameter selection algorithms.
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- author
- Langer, Andreas LU
- publishing date
- 2017-02-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Automated parameter selection, Constrained/unconstrained problem, Discrepancy principle, Gaussian noise, Impulse noise, Locally dependent regularization parameter
- in
- Journal of Mathematical Imaging and Vision
- volume
- 57
- issue
- 2
- pages
- 30 pages
- publisher
- Springer
- external identifiers
-
- scopus:84979620754
- ISSN
- 0924-9907
- DOI
- 10.1007/s10851-016-0676-2
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2016, Springer Science+Business Media New York. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
- id
- 32e68199-855e-4fc1-b577-e63643b53296
- date added to LUP
- 2021-03-15 22:31:41
- date last changed
- 2022-04-03 17:05:39
@article{32e68199-855e-4fc1-b577-e63643b53296,
abstract = {{<p>Algorithms for automatically selecting a scalar or locally varying regularization parameter for total variation models with an L<sup>τ</sup>-data fidelity term, τ∈ { 1 , 2 } , are presented. The automated selection of the regularization parameter is based on the discrepancy principle, whereby in each iteration a total variation model has to be minimized. In the case of a locally varying parameter, this amounts to solve a multiscale total variation minimization problem. For solving the constituted multiscale total variation model, convergent first- and second-order methods are introduced and analyzed. Numerical experiments for image denoising and image deblurring show the efficiency, the competitiveness, and the performance of the proposed fully automated scalar and locally varying parameter selection algorithms.</p>}},
author = {{Langer, Andreas}},
issn = {{0924-9907}},
keywords = {{Automated parameter selection; Constrained/unconstrained problem; Discrepancy principle; Gaussian noise; Impulse noise; Locally dependent regularization parameter}},
language = {{eng}},
month = {{02}},
number = {{2}},
pages = {{239--268}},
publisher = {{Springer}},
series = {{Journal of Mathematical Imaging and Vision}},
title = {{Automated Parameter Selection for Total Variation Minimization in Image Restoration}},
url = {{http://dx.doi.org/10.1007/s10851-016-0676-2}},
doi = {{10.1007/s10851-016-0676-2}},
volume = {{57}},
year = {{2017}},
}