Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters
arXiv:1703.03608
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.
References in corpus (6)
- The non-coplanar baselines effect in radio interferometry: The W-Projection algorithm
- Generalized SURE for Exponential Families: Applications to Regularization
- LOFAR Sparse Image Reconstruction
- Multi-Scale CLEAN: A comparison of its performance against classical CLEAN in galaxies using THINGS
- Distributed image reconstruction for very large arrays in radio astronomy
- A randomised primal-dual algorithm for distributed radio-interferometric imaging