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Open AccessArticle

Sparsity-Driven Reconstruction Technique for Microwave/Millimeter-Wave Computational Imaging

1
XLIM UMR 7252, Université de Limoges/CNRS, 87060 Limoges, France
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1536; https://doi.org/10.3390/s18051536
Received: 30 March 2018 / Revised: 24 April 2018 / Accepted: 1 May 2018 / Published: 12 May 2018
(This article belongs to the Special Issue Sensors for Microwave Imaging and Detection)
Numerous prototypes of computational imaging systems have recently been presented in the microwave and millimeter-wave domains, enabling the simplification of associated active architectures through the use of radiating cavities and metasurfaces that can multiplex signals encoded in the physical layer. This paper presents a new reconstruction technique leveraging the sparsity of the signals in the time-domain and decomposition of the sensing matrix by support detection, the size of the computational inverse problem being reduced significantly without compromising the image quality. View Full-Text
Keywords: computational imaging; short-range imaging; microwave; millimeter-wave; sparsity computational imaging; short-range imaging; microwave; millimeter-wave; sparsity
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MDPI and ACS Style

Fromenteze, T.; Decroze, C.; Abid, S.; Yurduseven, O. Sparsity-Driven Reconstruction Technique for Microwave/Millimeter-Wave Computational Imaging. Sensors 2018, 18, 1536. https://doi.org/10.3390/s18051536

AMA Style

Fromenteze T, Decroze C, Abid S, Yurduseven O. Sparsity-Driven Reconstruction Technique for Microwave/Millimeter-Wave Computational Imaging. Sensors. 2018; 18(5):1536. https://doi.org/10.3390/s18051536

Chicago/Turabian Style

Fromenteze, Thomas; Decroze, Cyril; Abid, Sana; Yurduseven, Okan. 2018. "Sparsity-Driven Reconstruction Technique for Microwave/Millimeter-Wave Computational Imaging" Sensors 18, no. 5: 1536. https://doi.org/10.3390/s18051536

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