Automated parameter selection in the L1-L2-TV model for removing Gaussian plus impulse noise
(2017) In Inverse Problems 33(7).- Abstract
The minimization of a functional consisting of a combined L 1/L 2-data-fidelity term and a total variation term, named L 1-L 2-TV model, is considered to remove a mixture of Gaussian and impulse noise in images, which are possibly additionally deformed by some convolution operator. We investigate analytically the stability of this model with respect to its parameters and link it to a constrained minimization problem. Based on these investigations and a statistical characterization of the mixed Gaussian-impulse noise a fully automated parameter selection algorithm for the L 1-L 2-TV model is presented. It is shown by numerical experiments that the proposed method finds... (More)
The minimization of a functional consisting of a combined L 1/L 2-data-fidelity term and a total variation term, named L 1-L 2-TV model, is considered to remove a mixture of Gaussian and impulse noise in images, which are possibly additionally deformed by some convolution operator. We investigate analytically the stability of this model with respect to its parameters and link it to a constrained minimization problem. Based on these investigations and a statistical characterization of the mixed Gaussian-impulse noise a fully automated parameter selection algorithm for the L 1-L 2-TV model is presented. It is shown by numerical experiments that the proposed method finds parameters with which noise is removed considerably while features are preserved in images.
(Less)
- author
- Langer, Andreas LU
- publishing date
- 2017-06-21
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- constrained/unconstrained problem, image reconstruction, mixed noise, parameter selection, total variation minimization
- in
- Inverse Problems
- volume
- 33
- issue
- 7
- article number
- 074002
- publisher
- IOP Publishing
- external identifiers
-
- scopus:85021757409
- ISSN
- 0266-5611
- DOI
- 10.1088/1361-6420/33/7/074002
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2017 IOP Publishing Ltd. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
- id
- 81c2c16c-77a0-409c-bb9b-4408518c5926
- date added to LUP
- 2021-03-15 22:30:31
- date last changed
- 2022-03-26 18:40:18
@article{81c2c16c-77a0-409c-bb9b-4408518c5926, 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 term, named L <sup>1</sup>-L <sup>2</sup>-TV model, is considered to remove a mixture of Gaussian and impulse noise in images, which are possibly additionally deformed by some convolution operator. We investigate analytically the stability of this model with respect to its parameters and link it to a constrained minimization problem. Based on these investigations and a statistical characterization of the mixed Gaussian-impulse noise a fully automated parameter selection algorithm for the L <sup>1</sup>-L <sup>2</sup>-TV model is presented. It is shown by numerical experiments that the proposed method finds parameters with which noise is removed considerably while features are preserved in images.</p>}}, author = {{Langer, Andreas}}, issn = {{0266-5611}}, keywords = {{constrained/unconstrained problem; image reconstruction; mixed noise; parameter selection; total variation minimization}}, language = {{eng}}, month = {{06}}, number = {{7}}, publisher = {{IOP Publishing}}, series = {{Inverse Problems}}, title = {{Automated parameter selection in the L<sup>1</sup>-L<sup>2</sup>-TV model for removing Gaussian plus impulse noise}}, url = {{http://dx.doi.org/10.1088/1361-6420/33/7/074002}}, doi = {{10.1088/1361-6420/33/7/074002}}, volume = {{33}}, year = {{2017}}, }