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Université Jean Monnet – Saint-Etienne |  |
Conference papers
Exact discrete minimization for TV+L0 image decomposition models
Abstract : Penalized maximum likelihood denoising approaches seek a solution that fulfills a compromise between data fidelity and agreement with a prior model. Penalization terms are generally chosen to enforce smoothness of the solution and to reject noise. The design of a proper penalization term is a difficult task as it has to capture image variability. Image decomposition into two components of different nature, each given a different penalty, is a way to enrich the modeling. We consider the decomposition of an image into a component with bounded variations and a sparse component. The corresponding penalization is the sum of the total variation of the first component and the L0 pseudo-norm of the second component. The minimization problem is highly non-convex, but can still be globally minimized by a minimum s-t-cut computation on a graph. The decomposition model is applied to synthetic aperture radar image denoising.
Cited literature [10 references]
https://hal-ujm.archives-ouvertes.fr/ujm-00985427
Contributor : Loïc Denis <>
Submitted on : Tuesday, April 29, 2014 - 4:25:30 PM
Last modification on : Monday, January 13, 2020 - 5:46:04 PM
Document(s) archivé(s) le : Tuesday, July 29, 2014 - 1:20:57 PM
Contributor : Loïc Denis <>
Submitted on : Tuesday, April 29, 2014 - 4:25:30 PM
Last modification on : Monday, January 13, 2020 - 5:46:04 PM
Document(s) archivé(s) le : Tuesday, July 29, 2014 - 1:20:57 PM
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