Solving inverse problems with overcomplete transforms and convex optimization techniques

Abstract

Many algorithms have been proposed during the last decade in order to deal with inverse problems. Of particular interest are convex optimization approaches that consist of minimizing a criteria generally composed of two terms: a data fidelity (linked to noise) term and a prior (regularization) term. As image properties are often easier to extract in a… (More)

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Cite this paper

@inproceedings{Chari2017SolvingIP, title={Solving inverse problems with overcomplete transforms and convex optimization techniques}, author={Lotfi Cha{\^a}ri and Nelly Pustelnik and Caroline Chaux and Jean-Christophe Pesquet}, year={2017} }