Compressive sensing with variable density sampling for 3D imaging

Adrian Stern, Vladislav Kravets, Yair Rivenson, Bahram Javidi

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

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.

Original languageEnglish
DOIs
StatePublished - 1 Jan 2019
EventThree-Dimensional Imaging, Visualization, and Display 2019 - Baltimore, United States
Duration: 15 Apr 201916 Apr 2019

Conference

ConferenceThree-Dimensional Imaging, Visualization, and Display 2019
Country/TerritoryUnited States
CityBaltimore
Period15/04/1916/04/19

Keywords

  • Compressive sensing
  • LIDAR
  • holography
  • variable random sensing

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