Next-generation radio interferometers like the Square Kilometer Array have the potential to
unlock scientific discoveries thanks to their unprecedented angular resolution and …

Accompanying Part I, this sequel delineates a validation of the recently proposed AI for
Regularization in radio-interferometric Imaging (AIRI) algorithm on observations from the …

A novel deep-learning paradigm for synthesis imaging by radio interferometry in astronomy
was recently proposed, dubbed" Residual-to-Residual DNN series for high-Dynamic range …

Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse
problems from large data volumes. Recent image reconstruction techniques grounded in …

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

We propose a new approach for large-scale high-dynamic range computational imaging.
Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging …

Context. Imaging in radio interferometry requires solving an ill-posed noisy inverse problem,
for which the most adopted algorithm is the original CLEAN algorithm and its variants …

Sampling-based algorithms are classical approaches to perform Bayesian inference in
inverse problems. They provide estimators with the associated credibility intervals to quantify …

Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when
solving inverse imaging problems. By learning a Deep Neural Network (DNN) behaving as a …

Aims: We address two issues for the adoption of convex optimization in radio interferometric
imaging. First, a method for a fine resolution setup is proposed which scales naturally in …