6 citations · 10 across the 8 of their papers we have counts for
8 papers
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…
Interlaced R2D2 DNN Series for Scalable Non-Cartesian MRI with Sensitivity Self-calibration
Shijie Chen, Yiwei Chen, Amir Aghabiglou +4
We introduce interlaced R2D2 (iR2D2), a DNN series paradigm for scalable image reconstruction from accelerated non-Cartesian k-space acquisitions in MRI with sensitivity map self-c…
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…
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2
Yiwei Chen, Chao Tang, Amir Aghabiglou +2
We propose a new approach for non-Cartesian magnetic resonance image reconstruction. While unrolled architectures provide robustness via data-consistency layers, embedding measurem…