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Next-generation radio interferometers, such as the Square Kilometre Array(SKA), will revolutionise our understanding of the universe through theirunprecedented sensitivity and resolution. However, to realise these goalssignificant challenges in image and data processing need to be overcome. Thestandard methods in radio interferometry for reconstructing images, such asCLEAN, have served the community well over the last few decades and havesurvived largely because they are pragmatic. However, they producereconstructed inter\-ferometric images that are limited in quality andscalability for big data. In this work we apply and evaluate alternativeinterferometric reconstruction methods that make use of state-of-the-art sparseimage reconstruction algorithms motivated by compressive sensing, which havebeen implemented in the PURIFY software package. In particular, we implementand apply the proximal alternating direction method of multipliers (P-ADMM)algorithm presented in a recent article. First, we assess the impact of theinterpolation kernel used to perform gridding and degridding on sparse imagereconstruction. We find that the Kaiser-Bessel interpolation kernel performs aswell as prolate spheroidal wave functions, while providing a computationalsaving and an analytic form. Second, we apply PURIFY to real interferometricobservations from the Very Large Array (VLA) and the Australia TelescopeCompact Array (ATCA) and find images recovered by PURIFY are higher qualitythan those recovered by CLEAN. Third, we discuss how PURIFY reconstructionsexhibit additional advantages over those recovered by CLEAN. The latest versionof PURIFY, with developments presented in this work, is made publiclyavailable.

Robust sparse image reconstruction of radio interferometric observations with purify