Radio interferometry allows astronomers to probe small spatial scales that are often
inaccessible with single-dish instruments. However, recovering the radio sky from an …

We introduce a new class of iterative image reconstruction algorithms for radio
interferometry, at the interface of convex optimization and deep learning, inspired by plug …

Uncertainty quantification is a critical missing component in radio interferometric imaging
that will only become increasingly important as the big-data era of radio interferometry …

This paper reviews parameterized CLEAN deconvolution, which is widely used in radio
synthesis imaging to remove the effects of sidelobes from the point-spread function caused …

Context. Reconstructing images from very long baseline interferometry (VLBI) data with a
sparse sampling of the Fourier domain (uv-coverage) constitutes an ill-posed deconvolution …

Strong gravitational lensing provides a wealth of astrophysical information on the baryonic
and dark matter content of galaxies. It also serves as a valuable cosmological probe by …

Uncertainty quantification is a critical missing component in radio interferometric imaging
that will only become increasingly important as the big-data era of radio interferometry …

Abstract We leverage the Sparsity Averaging Re-weighted Analysis approach for
interferometric imaging, that is based on convex optimization, for the super-resolution of Cyg …

To date weak gravitational lensing surveys have typically been restricted to small fields of
view, such that the flat-sky approximation has been sufficiently satisfied. However, with …

As Part I of a paper series showcasing a new imaging framework, we consider the recently
proposed unconstrained Sparsity Averaging Reweighted Analysis (uSARA) optimization …