Performance evaluation of popular l1-minimization algorithms in the context of Compressed Sensing

T. Bijeesh
{"title":"Performance evaluation of popular l1-minimization algorithms in the context of Compressed Sensing","authors":"T. Bijeesh","doi":"10.18495/COMENGAPP.V3I1.39","DOIUrl":null,"url":null,"abstract":"Compressed sensing (CS) is a data acquisition technique that is gaining popularity because of the fact that the reconstruction of the original signal is possible even if it was sampled at a sub-Nyquist rate. In contrast to the traditional sampling method, in CS we take a few measurements from the signal and the original signal can then be reconstructed from these measurements by using an optimization technique called l1 -minimization. Computer engineers and mathematician have been equally fascinated by this latest trend in digital signal processing. In this work we perform an evaluation of different l1 -minimization algorithms for their performance in reconstructing the signal in the context of CS. The algorithms that have been evaluated are PALM (Primal Augmented Lagrangian Multiplier method), DALM (Dual Augmented Lagrangian Multiplier method) and ISTA (Iterative Soft Thresholding Algorithm). The evaluation is done based on three parameters which are execution time, PSNR and RMSE.","PeriodicalId":120500,"journal":{"name":"Computer Engineering and Applications","volume":"140 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Engineering and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18495/COMENGAPP.V3I1.39","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Compressed sensing (CS) is a data acquisition technique that is gaining popularity because of the fact that the reconstruction of the original signal is possible even if it was sampled at a sub-Nyquist rate. In contrast to the traditional sampling method, in CS we take a few measurements from the signal and the original signal can then be reconstructed from these measurements by using an optimization technique called l1 -minimization. Computer engineers and mathematician have been equally fascinated by this latest trend in digital signal processing. In this work we perform an evaluation of different l1 -minimization algorithms for their performance in reconstructing the signal in the context of CS. The algorithms that have been evaluated are PALM (Primal Augmented Lagrangian Multiplier method), DALM (Dual Augmented Lagrangian Multiplier method) and ISTA (Iterative Soft Thresholding Algorithm). The evaluation is done based on three parameters which are execution time, PSNR and RMSE.
在压缩感知环境下流行的11 -最小化算法的性能评价
压缩感知(CS)是一种越来越受欢迎的数据采集技术,因为即使以亚奈奎斯特速率采样,也可以重建原始信号。与传统的采样方法相反,在CS中,我们从信号中进行一些测量,然后通过使用称为l1 -最小化的优化技术,可以从这些测量中重建原始信号。计算机工程师和数学家同样对数字信号处理的这一最新趋势着迷。在这项工作中,我们对不同的l1 -最小化算法在CS背景下重建信号的性能进行了评估。已经评估的算法有PALM(原始增广拉格朗日乘子法)、DALM(对偶增广拉格朗日乘子法)和ISTA(迭代软阈值算法)。评估基于三个参数:执行时间、PSNR和RMSE。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信