Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)

TL;DR

HyperSARA is a convex optimization-based method for wideband radio interferometric imaging that enhances resolution and sensitivity by exploiting low rankness and joint sparsity, outperforming traditional algorithms.

Contribution

Introduces HyperSARA, a novel convex optimization framework combining nuclear norm and joint sparsity minimization for improved wideband radio imaging.

Findings

Achieves higher resolution images than traditional methods.

Effectively suppresses artefacts across channels.

Demonstrates scalability to large datasets.

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, solves a sequence of weighted nuclear norm and l21 minimization problems promoting low rankness and joint average sparsity of the wideband model cube. On the one hand, enforcing low rankness enhances the overall resolution of the reconstructed model cube by exploiting the correlation between the different channels. On the other hand, promoting joint average sparsity improves the overall sensitivity by rejecting artefacts present on the different channels. An adaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem. The algorithmic structure is highly scalable to large data sets and allows for imaging in the presence of unknown noise levels and calibration errors. We showcase the superior performance of the proposed approach, reflected in high-resolution images on simulations and real VLA observations with respect to single channel imaging and the CLEAN-based wideband imaging algorithm in the WSCLEAN software. Our MATLAB code is available online on GITHUB.