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Found 5 papers, 0 papers with code
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.
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
We propose a new approach for non-Cartesian magnetic resonance image reconstruction.
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).
Deep network series for large-scale high-dynamic range imaging
We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input.
