PURIFY: a new algorithmic framework for next-generation radio-interferometric imaging

TL;DR

This paper introduces PURIFY, a novel convex optimization-based framework for radio-interferometric imaging that enhances scalability and speed, leveraging SDMM and sparsity priors like SARA for improved image reconstruction.

Contribution

It presents a new algorithmic structure using SDMM for scalable, high-dimensional radio-interferometric imaging, and demonstrates the effectiveness of the SARA sparsity prior.

Findings

PURIFY outperforms existing algorithms in simulations.

SARA sparsity prior shows superior reconstruction quality.

The framework enables high-dimensional data handling and acceleration.

Abstract

In recent works, compressed sensing (CS) and convex optimization techniques have been applied to radio-interferometric imaging showing the potential to outperform state-of-the-art imaging algorithms in the field. We review our latest contributions, which leverage the versatility of convex optimization to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to significant acceleration of the reconstruction and high-dimensional data scalability. The new algorithmic structure promoted in a new software PURIFY (beta version) relies on the simultaneous-direction method of multipliers (SDMM). The performance of various sparsity priors is evaluated through simulations in the continuous visibility setting, confirming the superiority of our recent average sparsity approach SARA.