Next Article in Journal
Hybrid Transverse Polar Navigation for High-Precision and Long-Term INSs
Next Article in Special Issue
Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data
Previous Article in Journal
An Effective Delay Reduction Approach through a Portion of Nodes with a Larger Duty Cycle for Industrial WSNs
Previous Article in Special Issue
Active Sensor for Microwave Tissue Imaging with Bias-Switched Arrays
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(5), 1536;

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

XLIM UMR 7252, Université de Limoges/CNRS, 87060 Limoges, France
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [4881 KB, uploaded 17 May 2018]   |  


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

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top