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Conference Papers Year : 2009

Minimization of a sparsity promoting criterion for the recovery of complex-valued signals

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Abstract

Ill-conditioned inverse problems are often encountered in signal/image processing. In this respect, convex objective functions including a sparsity promoting penalty term can be used. However, most of the existing optimization algorithms were developed for real-valued signals. In this paper, we are interested in complex-valued data. More precisely, we consider a class of penalty functions for which the associated regularized minimization problem can be solved numerically by a forward-backward algorithm. Functions within this class can be used to promote the sparsity of the solution. An application to parallel Magnetic Resonance Imaging (pMRI) reconstruction where complex-valued images are reconstructed is considered.
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Dates and versions

inria-00369590 , version 1 (20-03-2009)

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  • HAL Id : inria-00369590 , version 1

Cite

Lotfi Chaâri, Jean-Christophe Pesquet, Amel Benazza-Benyahia, Philippe Ciuciu. Minimization of a sparsity promoting criterion for the recovery of complex-valued signals. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369590⟩
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