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Multi-dimensional sparse structured signal approximation using split bregman iterations

Yoann Isaac 1, 2, 3 Quentin Barthélemy 4, 3 Cedric Gouy-Pailler 3 Jamal Atif 1, 2 Michèle Sebag 1, 2
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
3 LADIS - Laboratoire d'analyse des données et d'intelligence des systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
Abstract : The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization problem is tackled using a multi-dimensional extension of the split Bregman optimization approach. An extensive empirical evaluation shows how the proposed approach compares to the state of the art depending on the signal features.
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Yoann Isaac, Quentin Barthélemy, Cedric Gouy-Pailler, Jamal Atif, Michèle Sebag. Multi-dimensional sparse structured signal approximation using split bregman iterations. ICASSP 2013 - 38th IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, Vancouver, Canada. pp.3826-3830. ⟨hal-00862645⟩

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