An iterative algorithm with joint sparsity constraints for magnetic tomography (Articolo in rivista)

Type
Label
  • An iterative algorithm with joint sparsity constraints for magnetic tomography (Articolo in rivista) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Alternative label
  • Bretti G., Pitolli F. (2010)
    An iterative algorithm with joint sparsity constraints for magnetic tomography
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bretti G., Pitolli F. (literal)
Pagina inizio
  • 316 (literal)
Pagina fine
  • 328 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 5862 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Università La Sapienza di Roma Università Campus Bio-medico di Roma (literal)
Titolo
  • An iterative algorithm with joint sparsity constraints for magnetic tomography (literal)
Abstract
  • Magnetic tomography is an ill-posed and ill-conditioned inverse problem since, in general, the solution is non-unique and the measured magnetic field is affected by high noise. We use a joint sparsity constraint to regularize the magnetic inverse problem. This leads to a minimization problem whose solution can be approximated by an iterative thresholded Landweber algorithm. The algorithm is proved to be convergent and an error estimate is also given. Numerical tests on a bidimensional problem show that our algorithm outperforms Tikhonov regularization when the measurements are distorted by high noise. (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Prodotto
Autore CNR di
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
Insieme di parole chiave di
data.CNR.it