A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy

Pierre-Antoine Thouvenin, Abdullah Abdulaziz, Ming Jiang, Audrey Repetti, Arwa Dabbech, Yves Wiaux

Research output: Contribution to conferencePaper

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Abstract

Wideband radio-interferometric (RI) imaging consists in estimating images of the sky across a whole frequency band from incomplete Fourier data. Powerful prior information is needed to regularize the inverse imaging problem. At the extreme resolution and dynamic range of interest to modern telescopes, image cubes will far exceed Terabyte sizes, with data volumes orders of magnitude larger, making image estimation a very challenging task. The computational cost and memory requirements of corresponding iterative image recovery algorithms are extreme and call for high parallelism. A data-splitting strategy was recently introduced to parallelize computations over data blocks within an advanced primal-dual convex optimization algorithm. Building on the same algorithm, we propose an image faceting approach that consists in splitting the image cube into 3D overlapping facets with their own prior, reducing the computational bottleneck from full image to facet size. Simulation results suggest our prior provides similar if not superior reconstruction quality to the corresponding state-of-the-art non-faceted approach, with facet parallelization offering acceleration and therefore increased potential of scalability to large data and image sizes.
Original languageEnglish
Publication statusPublished - 16 Dec 2019
Event2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Le Gosier, Guadeloupe, Guadeloupe
Duration: 15 Dec 201918 Dec 2019
https://camsap19.ig.umons.ac.be/

Conference

Conference2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP 2019
CountryGuadeloupe
CityGuadeloupe
Period15/12/1918/12/19
Internet address

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Radio astronomy
Imaging techniques
Convex optimization
Telescopes
Frequency bands
Scalability
Data storage equipment
Recovery
Costs

Keywords

  • Wideband radio-interferometric imaging
  • facet-based prior
  • preconditioned primal-dual algorithm

Cite this

Thouvenin, P-A., Abdulaziz, A., Jiang, M., Repetti, A., Dabbech, A., & Wiaux, Y. (2019). A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. Paper presented at 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Guadeloupe, Guadeloupe.
Thouvenin, Pierre-Antoine ; Abdulaziz, Abdullah ; Jiang, Ming ; Repetti, Audrey ; Dabbech, Arwa ; Wiaux, Yves. / A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. Paper presented at 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Guadeloupe, Guadeloupe.
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Thouvenin, P-A, Abdulaziz, A, Jiang, M, Repetti, A, Dabbech, A & Wiaux, Y 2019, 'A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy', Paper presented at 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Guadeloupe, Guadeloupe, 15/12/19 - 18/12/19.

A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. / Thouvenin, Pierre-Antoine; Abdulaziz, Abdullah; Jiang, Ming; Repetti, Audrey; Dabbech, Arwa; Wiaux, Yves.

2019. Paper presented at 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Guadeloupe, Guadeloupe.

Research output: Contribution to conferencePaper

TY - CONF

T1 - A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy

AU - Thouvenin, Pierre-Antoine

AU - Abdulaziz, Abdullah

AU - Jiang, Ming

AU - Repetti, Audrey

AU - Dabbech, Arwa

AU - Wiaux, Yves

PY - 2019/12/16

Y1 - 2019/12/16

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AB - Wideband radio-interferometric (RI) imaging consists in estimating images of the sky across a whole frequency band from incomplete Fourier data. Powerful prior information is needed to regularize the inverse imaging problem. At the extreme resolution and dynamic range of interest to modern telescopes, image cubes will far exceed Terabyte sizes, with data volumes orders of magnitude larger, making image estimation a very challenging task. The computational cost and memory requirements of corresponding iterative image recovery algorithms are extreme and call for high parallelism. A data-splitting strategy was recently introduced to parallelize computations over data blocks within an advanced primal-dual convex optimization algorithm. Building on the same algorithm, we propose an image faceting approach that consists in splitting the image cube into 3D overlapping facets with their own prior, reducing the computational bottleneck from full image to facet size. Simulation results suggest our prior provides similar if not superior reconstruction quality to the corresponding state-of-the-art non-faceted approach, with facet parallelization offering acceleration and therefore increased potential of scalability to large data and image sizes.

KW - Wideband radio-interferometric imaging

KW - facet-based prior

KW - preconditioned primal-dual algorithm

M3 - Paper

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Thouvenin P-A, Abdulaziz A, Jiang M, Repetti A, Dabbech A, Wiaux Y. A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy. 2019. Paper presented at 2019 IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Guadeloupe, Guadeloupe.