PolyCLEAN: Atomic optimization for super-resolution imaging and uncertainty estimation in radio interferometry

¹ Audiovisual Communications Laboratory, École Polytechnique Fédérale de Lausanne, Switzerland
² Center for Imaging, École Polytechnique Fédérale de Lausanne, Switzerland
³ Centre For Research In Mathematics and Data Science, Western Sydney University, Australia
⁴ International Centre for Neuromorphic Systems, Western Sydney University, Australia

^★ Corresponding author; adrian.jarret@epfl.ch

Received: 3 June 2024
Accepted: 10 November 2024

Abstract

Context. Imaging in radio interferometry requires solving an ill-posed noisy inverse problem, for which the most adopted algorithm is the original CLEAN algorithm and its variants. Alternative explicit optimization methods have gained increasing attention, as they demonstrate excellent reconstruction quality thanks to their ability to enforce Bayesian priors. Nowadays, the main limitation to their adoption is run-time speed. Additionally, uncertainty quantification is difficult for both CLEAN and convex optimization techniques.

Aims. We address two issues for the adoption of convex optimization in radio interferometric imaging. First, we propose a method for a fine resolution setup, which scales naturally in terms of memory usage and reconstruction speed. Second, we develop a new tool to localize a region of uncertainty, paving the way for quantitative imaging in radio interferometry.

Methods. The classical ℓ₁ penalty is used to turn the inverse problem into a sparsity-promoting optimization. For efficient implementation, the so-called Frank-Wolfe algorithm is used together with a polyatomic refinement. The algorithm naturally produces sparse images at each iteration, leveraged to reduce memory and computational requirements. In that regard, PolyCLEAN reproduces the numerical behavior of CLEAN, while guaranteeing that it solves the minimization problem of interest. Additionally, we introduce the concept of the dual certificate image, which appears as a numerical byproduct of the Frank-Wolfe algorithm. This image is proposed as a tool for uncertainty quantification on the location of the recovered sources.

Results. PolyCLEAN demonstrates good scalability performance, in particular for fine-resolution grids. On simulations, the Pythonbased implementation is competitive with the fast numerically-optimized CLEAN solver. This acceleration does not affect image reconstruction quality: PolyCLEAN images are consistent with CLEAN-obtained ones for both point sources and diffuse emission recovery. We also highlight PolyCLEAN reconstruction capabilities on observed radio measurements.

Conclusions. PolyCLEAN can be considered as an alternative to CLEAN in the radio interferometric imaging pipeline, as it enables the use of Bayesian priors without impacting the scalability and numerical performance of the imaging method.