Title :
Semi-local total variation for regularization of inverse problems
Author_Institution :
Dept. Images & Signals, Univ. of Grenoble-Alpes, Grenoble, France
Abstract :
We propose the discrete semi-local total variation (SLTV) as a new regularization functional for inverse problems in imaging. The SLTV favors piecewise linear images; so the main drawback of the total variation (TV), its clustering effect, is avoided. Recently proposed primal-dual methods allow to solve the corresponding optimization problems as easily and efficiently as with the classical TV.
Keywords :
image reconstruction; inverse problems; minimisation; pattern clustering; SLTV; TV; clustering effect; discrete semilocal total variation; inverse problem regularization; optimization problems; piecewise linear images; primal-dual methods; Convex functions; Image reconstruction; Imaging; Inverse problems; Minimization; Signal processing algorithms; TV; convex optimization; inverse problem; non-local regularization; proximal method; total variation;
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
Conference_Location :
Lisbon
