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

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
StatePublished - 1 Dec 2016
Event24th European Signal Processing Conference 2016 - Budapest, Hungary

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

Fingerprint

Imaging techniques
Convex optimization
radio
Radio telescopes
Inverse problems
Signal processing
Tuning
signal processing
inverse problem
simulation

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. DOI: 10.1109/EUSIPCO.2016.7760276

Abdulaziz, Abdullah; Dabbech, Arwa; Onose, Alex; Wiaux, Yves / Low-rank and Joint-sparsity models for Hyperspectral Radio-Interferometric Imaging.

2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. p. 388-392 (European Signal Processing Conference (EUSIPCO)).

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

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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.",
author = "Abdullah Abdulaziz and Arwa Dabbech and Alex Onose and Yves Wiaux",
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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). European Signal Processing Conference (EUSIPCO), IEEE, pp. 388-392, 24th European Signal Processing Conference 2016, Budapest, Hungary, 29-2 September. DOI: 10.1109/EUSIPCO.2016.7760276

Low-rank and Joint-sparsity models for Hyperspectral Radio-Interferometric Imaging. / Abdulaziz, Abdullah; Dabbech, Arwa; Onose, Alex; Wiaux, Yves.

2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. p. 388-392 (European Signal Processing Conference (EUSIPCO)).

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

TY - CHAP

T1 - Low-rank and Joint-sparsity models for Hyperspectral Radio-Interferometric Imaging

AU - Abdulaziz,Abdullah

AU - Dabbech,Arwa

AU - Onose,Alex

AU - Wiaux,Yves

N1 - This work was supported by EPSRC, grants EP/M011089/1 and EP/M008843/1

PY - 2016/12/1

Y1 - 2016/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

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