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.