A faceted prior for scalable wideband computational imaging

Pierre-Antoine Thouvenin, Abdullah Abdulaziz, Ming Jiang, Audrey Repetti, 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 state-of-the-art solution consists in using a regularization term based on both the l2,1 and the nuclear norms, which however does not scale well with large numbers of spectral channels and huge image sizes. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior to improve the scalability of the associated imaging algorithm while preserving its reconstruction performance and better promoting local spectral correlations. We illustrate our approach on synthetic data in the context of radio-astronomy.
Original languageEnglish
Publication statusPublished - 2 Jul 2019
EventSignal Processing with Adaptive Sparse Structured Representations (SPARS) workshop - Toulouse, France
Duration: 1 Jul 20194 Jul 2019

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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Thouvenin, P-A., Abdulaziz, A., Jiang, M., Repetti, A., & Wiaux, Y. (2019). A faceted prior for scalable wideband computational imaging. Abstract from Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Toulouse, France.
Thouvenin, Pierre-Antoine ; Abdulaziz, Abdullah ; Jiang, Ming ; Repetti, Audrey ; 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 state-of-the-art solution consists in using a regularization term based on both the l2,1 and the nuclear norms, which however does not scale well with large numbers of spectral channels and huge image sizes. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior to improve the scalability of the associated imaging algorithm while preserving its reconstruction performance and better promoting local spectral correlations. We illustrate our approach on synthetic data in the context of radio-astronomy.",
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year = "2019",
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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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Thouvenin, P-A, Abdulaziz, A, Jiang, M, Repetti, A & 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. / Thouvenin, Pierre-Antoine; Abdulaziz, Abdullah; Jiang, Ming; Repetti, Audrey; 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 - Thouvenin, Pierre-Antoine

AU - Abdulaziz, Abdullah

AU - Jiang, Ming

AU - Repetti, Audrey

AU - Wiaux, Yves

PY - 2019/7/2

Y1 - 2019/7/2

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 state-of-the-art solution consists in using a regularization term based on both the l2,1 and the nuclear norms, which however does not scale well with large numbers of spectral channels and huge image sizes. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior to improve the scalability of the associated imaging algorithm while preserving its reconstruction performance and better promoting local spectral correlations. 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 state-of-the-art solution consists in using a regularization term based on both the l2,1 and the nuclear norms, which however does not scale well with large numbers of spectral channels and huge image sizes. To alleviate this issue, we propose a parallelizable faceted low-rankness and joint-sparsity prior to improve the scalability of the associated imaging algorithm while preserving its reconstruction performance and better promoting local spectral correlations. We illustrate our approach on synthetic data in the context of radio-astronomy.

M3 - Abstract

ER -

Thouvenin P-A, Abdulaziz A, Jiang M, Repetti A, Wiaux Y. A faceted prior for scalable wideband computational imaging. 2019. Abstract from Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Toulouse, France.