R2D2 image reconstruction with model uncertainty quantification in radio astronomy

arXiv:2403.18052v1 Announce Type: cross
Abstract: The ``Residual-to-Residual DNN series for high-Dynamic range imaging'' (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of Deep Neural Networks (DNNs) taking the previous iteration's image estimate and associated data residual as inputs. In this work, we investigate the robustness of the R2D2 image estimation process, by studying the uncertainty associated with its series of learned models. Adopting …

abstract arxiv astronomy astro-ph.im cs.lg dnn dynamic eess.iv eess.sp image images imaging iteration networks neural networks quantification radio residual series type uncertainty