Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging

TL;DR

The paper introduces SARA, a convex optimization algorithm that improves radio-interferometric image reconstruction by leveraging average sparsity across multiple wavelet bases, outperforming existing single-basis methods.

Contribution

SARA is a novel convex optimization algorithm that regularizes inverse problems with average sparsity over multiple wavelet bases, enhancing radio-interferometric imaging.

Findings

SARA outperforms state-of-the-art methods in simulations.

Utilizes multiple wavelet bases for improved sparsity regularization.

Demonstrates superior image reconstruction quality.

Abstract

We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse problem associated with the incomplete Fourier sampling identified by the visibility measurements is regularized by the assumption of average signal sparsity over representations in multiple wavelet bases. The algorithm, defined in the versatile framework of convex optimization, is dubbed Sparsity Averaging Reweighted Analysis (SARA). We show through simulations that the proposed approach outperforms state-of-the-art imaging methods in the field, which are based on the assumption of signal sparsity in a single basis only.