Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Dabbech, A"'
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 reconstruction is formed as a series of residual images, iteratively estimated
Externí odkaz:
http://arxiv.org/abs/2403.18052
Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging
Externí odkaz:
http://arxiv.org/abs/2403.05452
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 imaging" (R2D2). In this work, we start by shedding light on R2D2's algori
Externí odkaz:
http://arxiv.org/abs/2309.03291
Accompanying Part I, this sequel delineates a validation of the recently proposed AI for Regularisation in radio-interferometric Imaging (AIRI) algorithm on observations from the Australian Square Kilometre Array Pathfinder (ASKAP). The monochromatic
Externí odkaz:
http://arxiv.org/abs/2302.14149
As Part I of a paper series showcasing a new imaging framework, we consider the recently proposed unconstrained Sparsity Averaging Reweighted Analysis (uSARA) optimisation algorithm for wide-field, high-resolution, high-dynamic range, monochromatic i
Externí odkaz:
http://arxiv.org/abs/2302.14148
Autor:
Thouvenin, Pierre-Antoine, Dabbech, Arwa, Jiang, Ming, Abdulaziz, Abdullah, Thiran, Jean-Philippe, Jackson, Adrian, Wiaux, Yves
In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting f
Externí odkaz:
http://arxiv.org/abs/2209.07604
Autor:
Dabbech, Arwa, Terris, Matthieu, Jackson, Adrian, Ramatsoku, Mpati, Smirnov, Oleg M., Wiaux, Yves
Publikováno v:
2022 ApJL 939 L4
We introduce the first AI-based framework for deep, super-resolution, wide-field radio-interferometric imaging, and demonstrate it on observations of the ESO~137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image recon
Externí odkaz:
http://arxiv.org/abs/2207.11336
We introduce a new class of iterative image reconstruction algorithms for radio interferometry, at the interface of convex optimization and deep learning, inspired by plug-and-play methods. The approach consists in learning a prior image model by tra
Externí odkaz:
http://arxiv.org/abs/2202.12959
Radio interferometric (RI) data are noisy under-sampled spatial Fourier components of the unknown radio sky affected by direction-dependent antenna gains. Failure to model these antenna gains accurately results in a radio sky estimate with limited fi
Externí odkaz:
http://arxiv.org/abs/2102.00065
Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the Petabyte scale for SKA. Modern proximal optimization algorithms have shown a potential to signifi
Externí odkaz:
http://arxiv.org/abs/2003.07358