AIRI

AI for Regularisation in Imaging

Main codes & Tutorials

Papers & Codes

Toward a robust R2D2 paradigm for radio-interferometric imaging: revisiting DNN training and architecture
A. Aghabiglou, C. S. Chu, C. Tang, A. Dabbech, Y. Wiaux, ApJS, 280(2):63, 2025.
ArXiv:2503.02554v1| DOI:10.3847/1538-4365/adfbed

The AIRI plug-and-play algorithm for image reconstruction in radio-interferometry: variations and robustness
M. Terris , C. Tang , A. Jackson , Y. Wiaux, MNRAS, 537(2):1608-1619, 2025.
ArXiv:2312.07137 | DOI:10.1093/mnras/staf022

Scalable precision wide-field imaging in radio interferometry: II. AIRI validated on ASKAP data
A. Wilber, A. Dabbech, M. Terris, A. Jackson, and Y. Wiaux, MNRAS, 522(4):5576–5587, 2023.
ArXiv:2302.14149 | DOI:10.1093/mnras/stad1353

Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers
M. Terris, A. Dabbech, C. Tang, and Y. Wiaux, MNRAS, 518(1):604–622, 2023.
ArXiv:2202.12959| DOI:10.1093/mnras/stac2672

First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137-006
A. Dabbech, M. Terris, A. Jackson, M. Ramatsoku, O. Smirnov, and Y. Wiaux, ApJL, 939:L4, 2022.
ArXiv:2207.11336| DOI:10.3847/2041-8213/ac98af