Facet-Based Regularization for Scalable Radio-Interferometric Imaging

Conference Paper (2018)
Author(s)

Shahrzad Naghibzadeh (TU Delft - Signal Processing Systems)

Audrey Repetti (Heriot-Watt University)

AJ van der Veen (TU Delft - Signal Processing Systems)

Yves Wiaux (Heriot-Watt University)

Research Group
Signal Processing Systems
Copyright
© 2018 S. Naghibzadeh, Audrey Repetti, A.J. van der Veen, Yves Wiaux
DOI related publication
https://doi.org/10.23919/EUSIPCO.2018.8553515
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 S. Naghibzadeh, Audrey Repetti, A.J. van der Veen, Yves Wiaux
Research Group
Signal Processing Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
2678-2682
ISBN (print)
978-1-5386-3736-4
ISBN (electronic)
978-9-0827-9701-5
Reuse Rights

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Abstract

Current and future radio telescopes deal with large volumes of data and are expected to generate high resolution gigapixel-size images. The imaging problem in radio interferometry is highly ill-posed and the choice of prior model of the sky is of utmost importance to guarantee a reliable reconstruction. Traditionally, one or more regularization terms (e.g. sparsity and positivity) are applied for the complete image. However, radio sky images can often contain individual source facets in a large empty background. More precisely, we propose to divide radio images into source occupancy regions (facets) and apply relevant regularizing assumptions for each facet. Leveraging a stochastic primal dual algorithm, we show the potential merits of applying facet-based regularization on the radio-interferometric images which results in both computation time and memory requirement savings.

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