Compressive sensing with variable density sampling for 3D imaging

Adrian Stern; Vladislav Kravets; Yair Rivenson; Bahram Javidi

doi:10.1117/12.2521738

12 June 2019 Compressive sensing with variable density sampling for 3D imaging

Adrian Stern, Vladislav Kravets, Yair Rivenson, Bahram Javidi

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Proceedings Volume 10997, Three-Dimensional Imaging, Visualization, and Display 2019; 1099702 (2019) https://doi.org/10.1117/12.2521738
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

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Adrian Stern, Vladislav Kravets, Yair Rivenson, and Bahram Javidi "Compressive sensing with variable density sampling for 3D imaging", Proc. SPIE 10997, Three-Dimensional Imaging, Visualization, and Display 2019, 1099702 (12 June 2019); https://doi.org/10.1117/12.2521738

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