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

17 Downloads (Pure)

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, leverages low rankness, and joint average sparsity priors to enable formation of high-resolution and high-dynamic range image cubes from visibility data. The resulting minimization problem is solved using a primal-dual algorithm. The algorithmic structure is shipped with highly interesting functionalities such as preconditioning for accelerated convergence, and parallelization enabling to spread the computational cost and memory requirements across a multitude of processing nodes with limited resources. In this work, we provide a proof of concept for wideband image reconstruction of megabyte-size images. The better performance of HyperSARA, in terms of resolution and dynamic range of the formed images, compared to single channel imaging and the CLEAN-based wideband imaging algorithm in the WSCLEAN software, is showcased on simulations and Very Large Array observations. Our MATLAB code is available online on GITHUB.
Original languageEnglish
Pages (from-to)1230–1248
Number of pages19
JournalMonthly Notices of the Royal Astronomical Society
Volume489
Issue number1
Early online date5 Aug 2019
DOIs
Publication statusPublished - Oct 2019

Keywords

  • techniques: image processing
  • techniques: interferometric

Fingerprint Dive into the research topics of 'Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)'. Together they form a unique fingerprint.

Cite this