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Décomposition d’image par modèles variationnels : débruitage et extraction de texture

Abstract : This thesis is devoted in a first part to the elaboration of a second order variational modelfor image denoising, using the BV 2 space of bounded hessian functions. We here take a leaf out of the well known Rudin, Osher and Fatemi (ROF) model, where we replace the minimization of the total variation of the function with the minimization of the second order total variation of the function, that is to say the total variation of its partial derivatives. The goal is to get a competitive model with no staircasing effect that generates the ROF model anymore. The model we study seems to be efficient, but generates a blurry effect. In order to deal with it, we introduce a mixed model that permits to get solutions with no staircasing and without blurry effect on details. In a second part, we take an interset to the texture extraction problem. A model known as one of the most efficient is the T V -L1 model. It just consits in replacing the L2 norm of the fitting data term with the L1 norm.We propose here an original way to solve this problem by the use of augmented Lagrangian methods. For the same reason than for the denoising case, we also take an interest to the T V 2-L1 model, replacing again the total variation of the function by the second order total variation. A mixed model for texture extraction is finally briefly introduced. This manuscript ends with a huge chapter of numerical tests.
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Submitted on : Tuesday, June 28, 2011 - 1:20:46 PM
Last modification on : Thursday, March 5, 2020 - 6:49:25 PM
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  • HAL Id : tel-00598289, version 2



Loïc Piffet. Décomposition d’image par modèles variationnels : débruitage et extraction de texture. Mathématiques générales [math.GM]. Université d'Orléans, 2010. Français. ⟨NNT : 2010ORLE2053⟩. ⟨tel-00598289v2⟩



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