Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
This addresses a computational bottleneck for radio astronomy with the SKA, but it is incremental as it builds on an existing algorithm.
The paper tackles the challenge of automatically finding optimal regularization parameters for the MUFFIN algorithm in 3D radio-interferometric image reconstruction, proposing a self-tuned method using PSURE that scales well with large-scale data and demonstrates performance on a 3D image.
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.