An l1-Regularized Least-Squares Deblurring Algorithm with an Application to GPR Imaging

被引：0

作者：

Ogworonjo, Henry C. ^{[1
]}

Anderson, John M. M. ^{[1
]}

Wade, Mamadou ^{[1
]}

机构：

[1] Howard Univ, Dept Elect & Comp Engn, Washington, DC 20059 USA

来源：

2017 IEEE 8TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS AND MOBILE COMMUNICATION CONFERENCE (UEMCON) | 2017年

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The delay-and-sum (DAS) algorithm is widely used to reconstruct ground penetrating radar (GPR) images because of its simplicity and speed. However the drawback of the DAS algorithm is that it produces images with poor resolution and significant sidelobes. In this paper, we present a method that deblurs the DAS image using the least absolute shrinkage and selection operator (LASSO) without incurring the high computation cost associated with conventional LASSO. In a limited study using simulated data, the proposed deblurring algorithm produces images that are sparse with significantly reduced sidelobes.

引用

页码：604 / 607

页数：4

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