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 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.
- Publication:
-
Monthly Notices of the Royal Astronomical Society
- Pub Date:
- October 2019
- DOI:
- 10.1093/mnras/stz2117
- arXiv:
- arXiv:1806.04596
- Bibcode:
- 2019MNRAS.489.1230A
- Keywords:
-
- techniques: image processing;
- techniques: interferometric;
- Electrical Engineering and Systems Science - Image and Video Processing;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Electrical Engineering and Systems Science - Signal Processing
- E-Print:
- MNRAS, 08/2019