Compressive sensing with variable density sampling for 3D imaging

被引：3
|
作者：
Stern, Adrian [1 ]
Kravets, Vladislav [1 ]
Rivenson, Yair [2 ]
Javidi, Bahram [3 ]
机构：
[1] Ben Gurion Univ Negev, Sch Elect Engn & Comp Engn, Electroopt Dept, IL-84105 Beer Sheva, Israel
[2] Univ Calif Los Angeles, Dept Elect Engn & Comp Engn, Los Angeles, CA 90095 USA
[3] Univ Connecticut, Dept Elect & Comp Engn, U-2157, Storrs, CT 06269 USA
来源：
THREE-DIMENSIONAL IMAGING, VISUALIZATION, AND DISPLAY 2019 | 2019年 / 10997卷
关键词：
Compressive sensing; variable random sensing; holography; LIDAR;
D O I：
10.1117/12.2521738
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
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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页数：7
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