
Arwa DabbechHeriot-Watt University · Institute of Sensors, Signals and Systems
Arwa Dabbech
Phd in Sciences of the Universe
About
44
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607
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Introduction
Additional affiliations
June 2015 - present
November 2011 - April 2015
Publications
Publications (44)
Radio-interferometric 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 precision, well beyond CLEAN’s capability. These range from advanced proximal algorithms propelled by ha...
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 algorithmic structure, interpreting it as a learned version of CLEAN with minor cycles substituted with a...
We present a novel AI approach for high-resolution high-dynamic range synthesis imaging by radio interferometry (RI) in astronomy. R2D2, standing for "{R}esidual-to-{R}esidual {D}NN series for high-{D}ynamic range imaging", is a model-based data-driven approach relying on hybrid deep neural networks (DNNs) and data-consistency updates. Its reconstr...
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 AIRI-ASKAP images showcased in this work are formed using the same parallelised and automated imagi...
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 intensity imaging. We reconstruct images from real radio-interferometric observations obtained with t...
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 AIRI-ASKAP images showcased in this work are formed using the same parallelised and automated imagi...
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 intensity imaging. We reconstruct images from real radio-interferometric observations obtained with t...
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 significantly outperform CLEAN thanks to their ability to inject complex image models to regularize the inv...
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 functionality inherent to the underlying primal-dual forward-backward algorithm to decompose the imag...
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 reconstruction builds on a recent “plug-and-play” scheme whereby a denoising operator is injected as an im...
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 training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal re...
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 functionality inherent to the underlying primal-dual forward-backward algorithm to decompose the imag...
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 reconstruction builds on a recent "plug-and-play" scheme whereby a denoising operator is injected as an i...
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 training a deep neural network (DNN) as a denoiser, and substituting it for the handcrafted proximal re...
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 fidelity and resolution. The RI inverse problem has been recently addressed via a joint calibration an...
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 fidelity and resolution. The RI inverse problem has been recently addressed via a joint calibration an...
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 significantly outperform CLEAN thanks to their ability to inject complex image models to regularize the inv...
Variational-based methods are the state-of-the-art in sparse image deconvolution. Yet, this class of methods might not scale to large dimensions of interest in current high resolution imaging applications. To overcome this limitation, we propose to solve the sparse deconvo-lution problem through a two-step approach consisting in first solving (appr...
In the context of space-based optics, contamination due to particle deposition on the optics is inevitable and constitutes a critical issue. This gets more challenging for the sensitive heterodyne measurements of the Laser Interferometer Space Antenna (LISA), the space-based gravitational wave observatory to be launched in 2034. Therefore, table-to...
We propose a new approach within the versatile framework of convex optimization to solve the radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA, leverages low rankness and joint average sparsity priors to enable formation of high resolution and high dynamic range image cubes from visibility data. The resulting minimizati...
We propose a new approach within the versatile framework of convex optimization to solve the radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA, solves a sequence of weighted nuclear norm and $\ell_{2,1}$ minimization problems promoting low rankness and joint average sparsity of the wideband model cube. On the one hand,...
We leverage the SARA interferometric imaging technique based on convex optimisation for super-resolution of Cyg A from observations at X and C bands with the Karl G. Jansky Very Large Array (VLA). The associated average sparsity and positivity priors enable image reconstruction beyond instrumental resolution. An adaptive Preconditioned Primal-Dual...
We leverage the Sparsity Averaging Reweighted Analysis (SARA) approach for interferometric imaging, that is based on convex optimisation, for the super-resolution of Cyg A from observations at the frequencies 8.422GHz and 6.678GHz with the Karl G. Jansky Very Large Array (VLA). The associated average sparsity and positivity priors enable image reco...
Radio interferometric imaging aims to estimate an unknown sky intensity image from degraded observations, acquired through an antenna array. In the theoretical case of a perfectly calibrated array, it has been shown that solving the corresponding imaging problem by iterative algorithms based on convex optimization and compressive sensing theory can...
Extremely high data rates expected in next-generation radio interferometers necessitate a fast and robust way to process measurements in a big data context. Dimensionality reduction can alleviate computational load needed to process these data, in terms of both computing speed and memory usage. In this article, we present image reconstruction resul...
Modern radio telescopes, such as the Square Kilometre Array (SKA), will probe the radio sky over large fields-of-view, which results in large w-modulations of the sky image. This effect complicates the relationship between the measured visibilities and the image under scrutiny. In algorithmic terms, it gives rise to massive memory and computational...
Modern radio telescopes, such as the Square Kilometre Array (SKA), will probe the radio sky over large fields-of-view, which results in large w-modulations of the sky image. This effect complicates the relationship between the measured visibilities and the image under scrutiny. In algorithmic terms, it gives rise to massive memory and computational...
We propose a scalable, randomised algorithm to solve the inverse imaging problem in wide-band radio-interferometry. In the big-data context of the next-generation radio-telescopes, the scalability is paramount due to the large-scale of the problem to be solved. The proposed method distributes the data measured at each frequency and processes it in...
Radio-interferometric imaging aims to estimate an unknown sky intensity image from degraded observations, acquired through an antenna array. In the theoretical case of a perfectly calibrated array, it has been shown that solving the corresponding imaging problem by iterative algorithms based on convex optimization and compressive sensing theory can...
Next generation radio-interferometers, like the Square Kilometre Array, will acquire tremendous amounts of data with the goal of improving the size and sensitivity of the reconstructed images by orders of magnitude. The efficient processing of large-scale data sets is of great importance. We propose an acceleration strategy for a recently proposed...
Next generation radio-interferometers, like the Square Kilometre Array, will acquire tremendous amounts of data with the goal of improving the size and sensitivity of the reconstructed images by orders of magnitude. The efficient processing of large-scale data sets is of great importance. We propose an acceleration strategy for a recently proposed...
With the advent of the next-generation radio-interferometric telescopes like the Square Kilometre Array, novel signal processing methods are needed to provide the expected imaging resolution and sensitivity from extreme amounts of hyper-spectral data. In this context, we propose a generic non- parametric low-rank and joint-sparsity image model for...
The future radio-interferometric telescopes like the Square Kilometre Array will have un- precedented resolution, sensitivity and bandwidth. To take advantage of these powerful instruments and handle the extreme amounts of wide-band data, novel signal process- ing methods have to be tailored. In this respect, we present a generic non-parametric app...
Within the framework of the preparation for the Square Kilometre Array (SKA), that is the world largest radio telescope, new imaging challenges has to be conquered. The data acquired by SKA will have to be processed on real time because of their huge rate. In addition, thanks to its unprecedented resolution and sensitivity, SKA images will have ver...
NenuFAR is both a giant extension of the LOFAR and a large standalone instrument in the low-frequency range (10–85 MHz). It was designed in Nancay with national and international collaboration. Antenna radiators were modeled on the LWA antenna design whereas preamplifiers were designed in France. Antennas will be distributed in 96 mini-arrays of 19...
Galaxy clusters are known to host a variety of extended radio sources: tailed
radio galaxies whose shape is modelled by the interaction with the
intra-cluster medium (ICM); radio bubbles filling cavities in the ICM
distribution and rising buoyantly through the thermal gas; diffuse giant radio
sources ("halos" and "relics") revealing the presence of...
(arXiv abridged abstract) The current years are seeing huge developments of
radio telescopes and a tremendous increase of their capabilities. Such systems
make mandatory the design of more sophisticated techniques not only for
transporting, storing and processing this new generation of radio
interferometric data, but also for restoring the astrophy...
In the lead-up to the Square Kilometre Array (SKA) project, several
next-generation radio telescopes and upgrades are already being built around
the world. These include APERTIF (The Netherlands), ASKAP (Australia), eMERLIN
(UK), VLA (USA), e-EVN (based in Europe), LOFAR (The Netherlands), Meerkat
(South Africa), and the Murchison Widefield Array (...
This paper deals with the deconvolution of faint diffuse astronomical sources buried in the PSF sidelobes of surrounding bright compact sources, and in the noise. We propose a sparsity promoting restoration model which is based on highly redundant, shift invariant dictionaries, and which is hybrid in its sparsity priors. On one hand, the image to b...