Exact discrete minimization for TV+L0 image decomposition models

Loïc Denis 1, 2 Florence Tupin 3, 4 X. Rondeau 4
1 LHC - Laboratoire Hubert Curien [Saint Etienne]
2 CRAL - Centre de Recherche Astrophysique de Lyon
3 Télécom ParisTech
4 LTCI - Laboratoire Traitement et Communication de l'Information
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.
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Loïc Denis, Florence Tupin, X. Rondeau. Exact discrete minimization for TV+L0 image decomposition models. Image Processing (ICIP), 2010 17th IEEE International Conference on, Sep 2010, Hong Kong SAR China. pp.2525 - 2528, ⟨10.1109/ICIP.2010.5649204⟩. ⟨ujm-00985427⟩

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