6 citations · 10 across the 8 of their papers we have counts for
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S-R2D2: a spherical extension of the R2D2 deep neural network series paradigm for wide-field radio-interferometric imaging
A. Tajja, A. Aghabiglou, E. Tolley +3
Recently, the R2D2 paradigm, standing for ''Residual-to-Residual DNN series for high-Dynamic-range imaging'', was introduced for image formation in Radio Interferometry (RI) as a l…
Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture
Amir Aghabiglou, Chung San Chu, Chao Tang +2
The R2D2 Deep Neural Network (DNN) series was recently introduced for image formation in radio interferometry. It can be understood as a learned version of CLEAN, whose minor cycle…
The R2D2 deep neural network series paradigm for fast precision imaging in radio astronomy
Amir Aghabiglou, Chung San Chu, Arwa Dabbech +1
Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in o…
R2D2 image reconstruction with model uncertainty quantification in radio astronomy
Amir Aghabiglou, Chung San Chu, Arwa Dabbech +1
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 reconstru…
CLEANing Cygnus A deep and fast with R2D2
Arwa Dabbech, Amir Aghabiglou, Chung San Chu +1
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 imag…
Deep network series for large-scale high-dynamic range imaging
Amir Aghabiglou, Matthieu Terris, Adrian Jackson +1
We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost…