The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy

arXiv:2403.05452v1 Announce Type: cross
Abstract: Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denoisers, such as AIRI. Optimization and PnP structures are however highly iterative, which hinders their ability to …

abstract advanced algorithms arxiv astronomy astro-ph.im beyond capability cs.cv cs.lg data deep neural network dynamic image imaging network neural network optimization paradigm precision radio series theory type