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R2D2 image reconstruction with model uncertainty quantification in radio astronomy
The ``Residual-to-Residual DNN series for high-Dynamic range imaging'' (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy.
The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability.
CLEANing Cygnus A deep and fast with R2D2
A novel deep learning paradigm for synthesis imaging by radio interferometry in astronomy was recently proposed, dubbed "Residual-to-Residual DNN series for high-Dynamic range imaging" (R2D2).
Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
The approach consists in learning a prior image model by training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal regularization operator of an optimization algorithm.
Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)
We propose a new approach within the versatile framework of convex optimization to solve the radio-interferometric wideband imaging problem.
Image and Video Processing Instrumentation and Methods for Astrophysics Signal Processing
