International audienceWe 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
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
We propose the discrete semi-local total variation (SLTV) as a new regularization functional for inv...
International audienceWe propose the discrete semi-local total variation (SLTV) as a new regularizat...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
In this work, we introduce a function space setting for a wide class of structural/weighted total va...
International audienceIn the usual non-local variational models, such as the non-local total variati...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
International audienceIn the usual non-local variational models, such as the non-local total variati...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
Total-variation (TV) regularization is widely adopted in image restoration problems to exploit the f...
This article proposes a new framework to regularize imaging linear inverse problems using an adaptiv...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
We propose the discrete semi-local total variation (SLTV) as a new regularization functional for inv...
International audienceWe propose the discrete semi-local total variation (SLTV) as a new regularizat...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
Rédaction : fin 2011. Soutenance : mars 2012.Inverse problems are to recover the data that has been ...
In this work, we introduce a function space setting for a wide class of structural/weighted total va...
International audienceIn the usual non-local variational models, such as the non-local total variati...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
International audienceIn the usual non-local variational models, such as the non-local total variati...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
International audienceWe present iterative methods for choosing the optimal regularization parameter...
Total-variation (TV) regularization is widely adopted in image restoration problems to exploit the f...
This article proposes a new framework to regularize imaging linear inverse problems using an adaptiv...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...
An inverse problem is the process whereby data are used to identify unknown parameters in a system o...