Low-rank and Joint-sparsity models for Hyperspectral Radio-Interferometric Imaging

Abdullah Abdulaziz, Arwa Dabbech, Alex Onose, Yves Wiaux

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
144 Downloads (Pure)

Abstract

With the advent of the next-generation radio-interferometric telescopes, like the Square Kilometre Array, novel signal processing methods are needed to provide the expected imaging resolution and sensitivity from extreme amounts of hyper-spectral data. In this context, we propose a generic nonparametric low-rank and joint-sparsity image model for the regularisation of the associated wide-band inverse problem. We pose a convex optimisation problem and propose the use of an efficient algorithmic solver. The proposed optimisation task requires only one tuning parameter, namely the relative weight between the lowrank and joint-sparsity constraints. Our preliminary simulations suggest superior performance of the model with respect to separate single band imaging, as well as to other recently promoted non-parametric wide-band models leveraging convex optimisation.
Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages388-392
Number of pages5
ISBN (Electronic)9780992862657
DOIs
Publication statusPublished - 1 Dec 2016
Event24th European Signal Processing Conference 2016 - Hilton Budapest, Budapest, Hungary
Duration: 29 Aug 20162 Sep 2016
Conference number: 24

Publication series

NameEuropean Signal Processing Conference (EUSIPCO)
PublisherIEEE
ISSN (Print)2076-1465

Conference

Conference24th European Signal Processing Conference 2016
Abbreviated titleEUSIPCO 2016
CountryHungary
CityBudapest
Period29/08/162/09/16

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  • Cite this

    Abdulaziz, A., Dabbech, A., Onose, A., & Wiaux, Y. (2016). Low-rank and Joint-sparsity models for Hyperspectral Radio-Interferometric Imaging. In 2016 24th European Signal Processing Conference (EUSIPCO) (pp. 388-392). (European Signal Processing Conference (EUSIPCO)). IEEE. https://doi.org/10.1109/EUSIPCO.2016.7760276