Facet-Based Regularization for Scalable Radio-Interferometric Imaging

Shahrzad Naghibzadeh, Audrey Repetti, Alle-Jan van der Veen, Yves Wiaux

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)978-9-0827-9701-5
ISBN (Print)978-1-5386-3736-4
Publication statusPublished - 2018
EventEUSIPCO 2018: 26th European Signal Processing Conference - Rome, Italy
Duration: 3 Sept 20187 Sept 2018
Conference number: 26


ConferenceEUSIPCO 2018

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.


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