Home
Deutsch
Advanced Search
Browse by ...
Publication overview
Contact persons
Help
DOI
 |
|
Open Access Green as soon as Postprint is submitted to ZB.
Automated regularization parameter selection in multi-scale total variation models for image restoration.
J. Math. Imaging Vis. 40, 82-104 (2011)
Multi-scale total variation models for image restoration are introduced. The models utilize a spatially dependent regularization parameter in order to enhance image regions containing details while still sufficiently smoothing homogeneous features. The fully automated adjustment strategy of the regularization parameter is based on local variance estimators. For robustness reasons, the decision on the acceptance or rejection of a local parameter value relies on a confidence interval technique based on the expected maximal local variance estimate. In order to improve the performance of the initial algorithm a generalized hierarchical decomposition of the restored image is used. The corresponding subproblems are solved by a superlinearly convergent algorithm based on Fenchel-duality and inexact semismooth Newton techniques. The paper ends by a report on numerical tests, a qualitative study of the proposed adjustment scheme and a comparison with popular total variation based restoration methods.
Altmetric
Additional Metrics?
Edit extra informations
Login
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Local variance estimator; Hierarchical decomposition; Order statistics; Total variation regularization; Primal-dual method; Semismooth Newton method; Spatially dependent regularization parameter
ISSN (print) / ISBN
0924-9907
e-ISSN
1573-7683
Quellenangaben
Volume: 40,
Issue: 1,
Pages: 82-104
Publisher
Springer
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Biomathematics and Biometry (IBB)