SARA-PPD is a proof of concept MATLAB implementation of an acceleration strategy for a recently proposed primal-dual distributed algorithm. The algorithm optimizes resolution by accounting for the correct noise statistics, leverages natural weighting in the definition of the minimization problem for image reconstruction, and optimizes sensitivity by enabling accelerated convergence through a preconditioning strategy incorporating sampling density information. This algorithm offers efficient processing of large-scale data sets that will be acquired by next generation radio-interferometers such as the Square Kilometer Array.

Puri-Psi addresses radio interferometric imaging problems using state-of-the-art optimization algorithms and deep learning. It performs scalable monochromatic, wide-band, and polarized imaging. It also provide joint calibration and imaging, and scalable uncertainty quantification. A scalable framework for wide-field monochromatic intensity imaging is also available, which encompasses a pure optimization algorithm, as well as an AI-based method in the form of a plug-and-play algorithm propelled by Deep Neural Network denoisers.

Faceted-HyperSARA images radio-interferometric wideband intensity data. Written in MATLAB, the library offers a collection of utility functions and scripts from data extraction from an RI measurement set MS Table to the reconstruction of a wideband intensity image over the field of view and frequency range of interest. The code achieves high precision imaging from large data volumes and supports data dimensionality reduction via visibility gridding and estimation of the effective noise level when reliable noise estimates are not available. Faceted-HyperSASA also corrects the w-term via w-projection and incorporates available compact Fourier models of the direction dependent effects (DDEs) in the measurement operator.

R2D2 (Residual-to-Residual DNN series for high-Dynamic range imaging) performs synthesis imaging for radio interferometry. The R2D2 algorithm takes a hybrid structure between a Plug-and-Play (PnP) algorithm and a learned version of the well-known Matching Pursuit algorithm. Its reconstruction is formed as a series of residual images, iteratively estimated as outputs of iteration-specific Deep Neural Networks (DNNs), each taking the previous iteration’s image estimate and associated back-projected data residual as inputs. The primary application of the R2D2 algorithm is to solve large-scale high-resolution high-dynamic range inverse problems in radio astronomy, more specifically 2D planar monochromatic intensity imaging.