Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters

TL;DR

This paper introduces a self-tuning regularization parameter method for the MUFFIN algorithm, enabling efficient and scalable 3D radio interferometric image reconstruction for large datasets like those from SKA.

Contribution

It presents a novel self-tuning approach using PSURE for optimal regularization in MUFFIN, improving scalability and automation in large-scale radio interferometry image reconstruction.

Findings

The method effectively finds regularization parameters without ground truth.

Scales well with large 3D datasets.

Numerical results demonstrate improved reconstruction performance.

Abstract

As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For radio INterferometry). In particular, we focus on the challenging task of automatically finding the optimal regularization parameter values. In practice, finding the regularization parameters using classical grid search is computationally intensive and nontrivial due to the lack of ground- truth. We adopt a greedy strategy where, at each iteration, the optimal parameters are found by minimizing the predicted Stein unbiased risk estimate (PSURE). The proposed self-tuned version of MUFFIN involves parallel and computationally efficient steps, and scales well with large- scale data. Finally, numerical results on a 3D image are presented to showcase the performance of the proposed approach.