Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)

Abdullah Abdulaziz, Arwa Dabbech, Yves Wiaux

Research output: Contribution to journalArticle

Abstract

We propose a new approach within the versatile framework of convex optimization to solve
the radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA,
solves a sequence of weighted nuclear norm and `2,1 minimization problems promoting low
rankness and joint average sparsity of the wideband model cube. On the one hand, enforcing
low rankness enhances the overall resolution of the reconstructed model cube by exploiting the
correlation between the different channels. On the other hand, promoting joint average sparsity
improves the overall sensitivity by rejecting artefacts present on the different channels. An
adaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem.
The algorithmic structure is highly scalable to large data sets and allows for imaging in the
presence of unknown noise levels and calibration errors.We showcase the superior performance
of the proposed approach, reflected in high-resolution images on simulations and real VLA
observations with respect to single channel imaging and the clean-based wideband imaging
algorithm in the wsclean software. Our matlab code is available online on github.
LanguageEnglish
JournalMonthly Notices of the Royal Astronomical Society
Publication statusSubmitted - 11 Jun 2018

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Interferometry
Imaging techniques
Convex optimization
Image resolution
Calibration

Keywords

  • techniques: image processing
  • techniques: interferometric

Cite this

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title = "Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)",
abstract = "We propose a new approach within the versatile framework of convex optimization to solvethe radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA,solves a sequence of weighted nuclear norm and `2,1 minimization problems promoting lowrankness and joint average sparsity of the wideband model cube. On the one hand, enforcinglow rankness enhances the overall resolution of the reconstructed model cube by exploiting thecorrelation between the different channels. On the other hand, promoting joint average sparsityimproves the overall sensitivity by rejecting artefacts present on the different channels. Anadaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem.The algorithmic structure is highly scalable to large data sets and allows for imaging in thepresence of unknown noise levels and calibration errors.We showcase the superior performanceof the proposed approach, reflected in high-resolution images on simulations and real VLAobservations with respect to single channel imaging and the clean-based wideband imagingalgorithm in the wsclean software. Our matlab code is available online on github.",
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author = "Abdullah Abdulaziz and Arwa Dabbech and Yves Wiaux",
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journal = "Monthly Notices of the Royal Astronomical Society",
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T1 - Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)

AU - Abdulaziz, Abdullah

AU - Dabbech, Arwa

AU - Wiaux, Yves

PY - 2018/6/11

Y1 - 2018/6/11

N2 - We propose a new approach within the versatile framework of convex optimization to solvethe radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA,solves a sequence of weighted nuclear norm and `2,1 minimization problems promoting lowrankness and joint average sparsity of the wideband model cube. On the one hand, enforcinglow rankness enhances the overall resolution of the reconstructed model cube by exploiting thecorrelation between the different channels. On the other hand, promoting joint average sparsityimproves the overall sensitivity by rejecting artefacts present on the different channels. Anadaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem.The algorithmic structure is highly scalable to large data sets and allows for imaging in thepresence of unknown noise levels and calibration errors.We showcase the superior performanceof the proposed approach, reflected in high-resolution images on simulations and real VLAobservations with respect to single channel imaging and the clean-based wideband imagingalgorithm in the wsclean software. Our matlab code is available online on github.

AB - We propose a new approach within the versatile framework of convex optimization to solvethe radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA,solves a sequence of weighted nuclear norm and `2,1 minimization problems promoting lowrankness and joint average sparsity of the wideband model cube. On the one hand, enforcinglow rankness enhances the overall resolution of the reconstructed model cube by exploiting thecorrelation between the different channels. On the other hand, promoting joint average sparsityimproves the overall sensitivity by rejecting artefacts present on the different channels. Anadaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem.The algorithmic structure is highly scalable to large data sets and allows for imaging in thepresence of unknown noise levels and calibration errors.We showcase the superior performanceof the proposed approach, reflected in high-resolution images on simulations and real VLAobservations with respect to single channel imaging and the clean-based wideband imagingalgorithm in the wsclean software. Our matlab code is available online on github.

KW - techniques: image processing

KW - techniques: interferometric

M3 - Article

JO - Monthly Notices of the Royal Astronomical Society

T2 - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

ER -