Titelaufnahme

Titel
A smoothing descent method for nonconvex TVq-Models
Verfasser/ VerfasserinHintermüller, Michael In der Gemeinsamen Normdatei der DNB nachschlagen ; Wu, Tao
Erschienen in
Lecture notes in computer science, Berlin [u.a.] : Springer, 1.1973 -, S. 119-133
SpracheEnglisch
DokumenttypAufsatz in einem Sammelwerk
ISBN978-3-642-54774-4
ISSN0302-9743
URNurn:nbn:at:at-ubg:3-792 Persistent Identifier (URN)
Zugriffsbeschränkung
 Das Werk ist frei verfügbar
Dateien
A smoothing descent method for nonconvex TVq-Models [0.56 mb]
Links
Nachweis
Klassifikation
Zusammenfassung (Englisch)

A novel class of variational models with nonconvex q -norm-type regularizations ( 0<q<1 ) is considered, which typically outperforms popular models with convex regularizations in restoring sparse images. Due to the fact that the objective function is nonconvex and non-Lipschitz, such models are very challenging from an analytical as well as numerical point of view. In this work a smoothing descent method with provable convergence properties is proposed for computing stationary points of the underlying variational problem. Numerical experiments are reported to illustrate the effectiveness of the new method.

Notiz