Compressive Sensing as Applied to Inverse Problems for Imaging: Theory, applications, current trends, and open challenges.

Abstract : Compressive sensing (CS) is currently one the most active research fields in information engineering and science. The flexibility, robustness, accuracy, effectiveness, and sound theory behind such a paradigm have motivated a great interest in developing and applying CS to many domains, including inverse scattering. Unfortunately, electromagnetic imaging problems have some unique theoretical features that prevent a straightforward exploitation of CS tools. Therefore, suitable CS-based strategies must be considered in such a framework.
Type de document :
Article dans une revue
IEEE Antennas and Propagation Magazine, Institute of Electrical and Electronics Engineers, 2017, IEEE Antennas and Propagation Magazine, 59 (5), pp.34 - 46. 〈http://ieeexplore.ieee.org/document/8015117/〉. 〈10.1109/MAP.2017.2731204〉
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Contributeur : Andrea Massa <>
Soumis le : mercredi 6 décembre 2017 - 10:19:38
Dernière modification le : jeudi 5 avril 2018 - 12:30:06

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Giacomo Oliveri, Marco Salucci, Nicola Anselmi, Andrea Massa. Compressive Sensing as Applied to Inverse Problems for Imaging: Theory, applications, current trends, and open challenges.. IEEE Antennas and Propagation Magazine, Institute of Electrical and Electronics Engineers, 2017, IEEE Antennas and Propagation Magazine, 59 (5), pp.34 - 46. 〈http://ieeexplore.ieee.org/document/8015117/〉. 〈10.1109/MAP.2017.2731204〉. 〈hal-01656851〉

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