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Compressive sensing with variable density sampling for 3D imaging
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Paper Abstract

Compressive Sensing (CS) can alleviate the sensing effort involved in the acquisition of three dimensional image (3D) data. The most common CS sampling schemes employ uniformly random sampling because it is universal, thus it is applicable to almost any signals. However, by considering general properties of images and properties of the acquisition mechanism, it is possible to design random sampling schemes with variable density that have improved CS performance. We have introduced the concept of non-uniform CS random sampling a decade ago for holography. In this paper we overview the non-uniform CS random concept evolution and application for coherent holography, incoherent holography and for 3D LiDAR imaging.

Paper Details

Date Published: 12 June 2019
PDF: 7 pages
Proc. SPIE 10997, Three-Dimensional Imaging, Visualization, and Display 2019, 1099702 (12 June 2019); doi: 10.1117/12.2521738
Show Author Affiliations
Adrian Stern, Ben-Gurion Univ. of the Negev (Israel)
Vladislav Kravets, Ben-Gurion Univ. of the Negev (Israel)
Yair Rivenson, Univ. of California, Los Angeles (United States)
Bahram Javidi, Univ. of Connecticut (United States)


Published in SPIE Proceedings Vol. 10997:
Three-Dimensional Imaging, Visualization, and Display 2019
Bahram Javidi; Jung-Young Son; Osamu Matoba, Editor(s)

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