Electric current density imaging via an accelerated iterative algorithm with joint sparsity constraints

Gabriella Bretti 1 Massimo Fornasier 2 Francesca Pitolli 1
1 MeMoMat - Dipartimento di Metodi e Modelli Matematici per le Scienze Applicate
2 RICAM - Johann Radon Institute for Computational and Applied Mathematics
Abstract : Many problems in applied sciences require to spatially resolve an unknown electrical current distribution from its external magnetic field. Electric currents emit magnetic fields which can be measured by sophisticated superconducting devices in a noninvasive way. Applications of this technique arise in several fields, such as medical imaging and non-destructive testing, and they involve the solution of an inverse problem. Assuming that each component of the current density vector possesses the same sparse representation with respect to a preassigned multiscale basis, allows us to apply new regularization techniques to the magnetic inverse problem. The solution of linear inverse problems with sparsity constraints can be efficiently obtained by iterative algorithms based on gradient steps intertwined with thresholding operations. We test this algorithms to numerically solve the magnetic inverse problem with a joint sparsity constraint.
Type de document :
Communication dans un congrès
Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, France. 2009
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/inria-00369432
Contributeur : Ist Rennes <>
Soumis le : jeudi 19 mars 2009 - 17:09:13
Dernière modification le : lundi 20 juin 2016 - 14:10:32
Document(s) archivé(s) le : vendredi 12 octobre 2012 - 13:50:55

Fichier

79.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00369432, version 1

Collections

SPARS09

Citation

Gabriella Bretti, Massimo Fornasier, Francesca Pitolli. Electric current density imaging via an accelerated iterative algorithm with joint sparsity constraints. Rémi Gribonval. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint Malo, France. 2009. 〈inria-00369432〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

131

Téléchargements de fichiers

219

Contact


archive-ouverte@inria.fr