Why CLEAN when you can PURIFY? A new approach for next-generation radio-interferometric imaging

TL;DR

This paper introduces a novel convex optimization-based approach for radio-interferometric imaging that improves scalability and handles continuous visibilities, leveraging average sparsity and parallelizable algorithms.

Contribution

It presents a new scalable convex optimization framework using average sparsity (SARA) for high-dimensional radio-interferometric imaging.

Findings

Outperforms traditional algorithms in accuracy.

Handles realistic continuous visibilities effectively.

Offers a highly parallelizable implementation.

Abstract

In recent works, sparse models and convex optimization techniques have been applied to radio-interferometric (RI) imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. In this talk, I will review our latest contributions in RI imaging, which leverage the versatility of convex optimization to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to high-dimensional data scalability. Firstly, I will review our recently proposed average sparsity approach, SARA, which relies on the observation that natural images exhibit strong average sparsity over multiple coherent bases. Secondly, I will discuss efficient implementations of SARA, and sparse regularization problems in general, for large-scale imaging problems in a new toolbox dubbed