4 papers
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…
HyperAIRI: a plug-and-play algorithm for precise hyperspectral image reconstruction in radio interferometry
Chao Tang, Arwa Dabbech, Adrian Jackson +1
The next-generation radio-interferometric (RI) telescopes require imaging algorithms capable of forming high-resolution high-dynamic-range images from large data volumes spanning w…
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…
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…