A Faceted Prior for Scalable Wideband Computational Imaging

Abdullah Abdulaziz, Pierre-Antoine Thouvenin, Ming Jiang, Yves Wiaux

Research output: Contribution to conferenceAbstract

Abstract

Hyperspectral images exhibit strong spectral correlations,which can be exploited via a low-rankness and joint-sparsity prior when reconstructed from incomplete and noisy measurements. A reliable solution consists in using a regularization term based on the l2,1 and the nuclear norms, which however does not scale well with the number of spectral channels. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior, which can be exploited to improve the scalability of the associated imaging algorithm. We illustrate our approach on synthetic data in the context of radio-astronomy.

Workshop

WorkshopSignal Processing with Adaptive Sparse Structured Representations (SPARS) workshop
Abbreviated titleSPARS 2019
CountryFrance
CityToulouse
Period1/07/194/07/19

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astronomy
radio
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Cite this

Abdulaziz, A., Thouvenin, P-A., Jiang, M., & Wiaux, Y. (2019). A Faceted Prior for Scalable Wideband Computational Imaging. Abstract from Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Toulouse, France.
Abdulaziz, Abdullah ; Thouvenin, Pierre-Antoine ; Jiang, Ming ; Wiaux, Yves. / A Faceted Prior for Scalable Wideband Computational Imaging. Abstract from Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Toulouse, France.
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title = "A Faceted Prior for Scalable Wideband Computational Imaging",
abstract = "Hyperspectral images exhibit strong spectral correlations,which can be exploited via a low-rankness and joint-sparsity prior when reconstructed from incomplete and noisy measurements. A reliable solution consists in using a regularization term based on the l2,1 and the nuclear norms, which however does not scale well with the number of spectral channels. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior, which can be exploited to improve the scalability of the associated imaging algorithm. We illustrate our approach on synthetic data in the context of radio-astronomy.",
author = "Abdullah Abdulaziz and Pierre-Antoine Thouvenin and Ming Jiang and Yves Wiaux",
year = "2019",
month = "4",
day = "1",
language = "English",
note = "Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, SPARS 2019 ; Conference date: 01-07-2019 Through 04-07-2019",

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Abdulaziz, A, Thouvenin, P-A, Jiang, M & Wiaux, Y 2019, 'A Faceted Prior for Scalable Wideband Computational Imaging' Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Toulouse, France, 1/07/19 - 4/07/19, .

A Faceted Prior for Scalable Wideband Computational Imaging. / Abdulaziz, Abdullah; Thouvenin, Pierre-Antoine; Jiang, Ming; Wiaux, Yves.

2019. Abstract from Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Toulouse, France.

Research output: Contribution to conferenceAbstract

TY - CONF

T1 - A Faceted Prior for Scalable Wideband Computational Imaging

AU - Abdulaziz, Abdullah

AU - Thouvenin, Pierre-Antoine

AU - Jiang, Ming

AU - Wiaux, Yves

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Hyperspectral images exhibit strong spectral correlations,which can be exploited via a low-rankness and joint-sparsity prior when reconstructed from incomplete and noisy measurements. A reliable solution consists in using a regularization term based on the l2,1 and the nuclear norms, which however does not scale well with the number of spectral channels. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior, which can be exploited to improve the scalability of the associated imaging algorithm. We illustrate our approach on synthetic data in the context of radio-astronomy.

AB - Hyperspectral images exhibit strong spectral correlations,which can be exploited via a low-rankness and joint-sparsity prior when reconstructed from incomplete and noisy measurements. A reliable solution consists in using a regularization term based on the l2,1 and the nuclear norms, which however does not scale well with the number of spectral channels. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior, which can be exploited to improve the scalability of the associated imaging algorithm. We illustrate our approach on synthetic data in the context of radio-astronomy.

M3 - Abstract

ER -

Abdulaziz A, Thouvenin P-A, Jiang M, Wiaux Y. A Faceted Prior for Scalable Wideband Computational Imaging. 2019. Abstract from Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Toulouse, France.