Joint Multichannel Deconvolution and Blind Source Separation - IRFU-AIM Accéder directement au contenu
Article Dans Une Revue SIAM Journal on Imaging Sciences Année : 2017

Joint Multichannel Deconvolution and Blind Source Separation

Ming Jiang
  • Fonction : Auteur
  • PersonId : 1094298
Jerome Bobin
  • Fonction : Auteur
  • PersonId : 858908
Jean-Luc Starck
  • Fonction : Auteur
  • PersonId : 858909

Résumé

Blind Source Separation (BSS) is a challenging matrix factorization problem that plays a central role in multichannel imaging science. In a large number of applications, such as astrophysics, current unmixing methods are limited since real-world mixtures are generally affected by extra instrumental effects like blurring. Therefore, BSS has to be solved jointly with a deconvolution problem, which requires tackling a new inverse problem: deconvolution BSS (DBSS). In this article, we introduce an innovative DBSS approach, called DecGMCA, based on sparse signal modeling and an efficient alternative projected least square algorithm. Numerical results demonstrate that the DecGMCA algorithm performs very well on simulations. It further highlights the importance of jointly solving BSS and deconvolution instead of considering these two problems independently. Furthermore, the performance of the proposed DecGMCA algorithm is demonstrated on simulated radio-interferometric data.
Fichier principal
Vignette du fichier
Jiang17.pdf (3.2 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03177885 , version 1 (23-03-2021)

Identifiants

Citer

Ming Jiang, Jerome Bobin, Jean-Luc Starck. Joint Multichannel Deconvolution and Blind Source Separation. SIAM Journal on Imaging Sciences, 2017, 10 (4), pp.1997-2021. ⟨10.1137/16M1103713⟩. ⟨hal-03177885⟩
22 Consultations
19 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More