2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
The AIRI plug-and-play algorithm for image reconstruction in radio-interferometry: variations and robustness
Matthieu Terris, Chao Tang, Adrian Jackson +1
Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when solving inverse imaging problems. By learning a Deep Neural Network (DNN) denoiser behaving as…