This paper presents a multilevel algorithm specifically designed for radio-interferometric imaging in astronomy. The proposed algorithm is used to solve the uSARA (unconstrained Sparsity Averaging Reweighting Analysis) formulation of this image restoration problem. Multilevel algorithms rely on a hierarchy of approximations of the objective function to accelerate its optimization. In contrast to the usual multilevel approaches where this hierarchy is derived in the parameter space, here we construct the hierarchy of approximations in the observation space. The proposed approach is compared to a reweighted forward-backward procedure, which is the backbone iteration scheme for solving the uSARA problem.
@article{arxiv.2403.13385,
title = {A multilevel framework for accelerating uSARA in radio-interferometric imaging},
author = {Guillaume Lauga and Audrey Repetti and Elisa Riccietti and Nelly Pustelnik and Paulo Gonçalves and Yves Wiaux},
journal= {arXiv preprint arXiv:2403.13385},
year = {2024}
}