Titelaufnahme

Titel
Automated regularization parameter selection in multi-scale total variation models for image restoration
Verfasser/ VerfasserinDong, Yiqiu ; Hintermüller, Michael In der Gemeinsamen Normdatei der DNB nachschlagen ; Rincon-Camacho, M. Monserrat
Erschienen in
Journal of Mathematical Imaging and Vision, Dordrecht [u.a.], 2011, Jg. 40, H. 1, S. 82-104
ErschienenSpringer
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)Local variance estimator / Hierarchical decomposition / Order statistics / Total variation regularization / Primal-dual method / Semismooth Newton method / Spatially dependent regularization parameter
Schlagwörter (GND)Bildrekonstruktion / Bildverarbeitung / Newton-Verfahren / Online-Publikation
URNurn:nbn:at:at-ubg:3-814 Persistent Identifier (URN)
DOIdoi:10.1007/s10851-010-0248-9 
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Automated regularization parameter selection in multi-scale total variation models for image restoration [5.13 mb]
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Zusammenfassung (Englisch)

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.

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