Next Article in Journal
The Expansion Cracks of Dolomitic Aggregates Cured in TMAH Solution Caused by Alkali–Carbonate Reaction
Previous Article in Journal
Effect of Jute Fiber Modification on Mechanical Properties of Jute Fiber Composite
Previous Article in Special Issue
A Label-Free Fluorescent DNA Machine for Sensitive Cyclic Amplification Detection of ATP
Article Menu

Export Article

Open AccessArticle
Materials 2019, 12(8), 1227; https://doi.org/10.3390/ma12081227

A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l0 Norm and Modified Newton Method

1, 1,2,*, 3 and 2
1
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
2
The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems Integration, CRRC Zhuzhou Locomotive Co., Ltd., Zhuzhou 412001, China
3
China Mobile (Suzhou) Software Technology Co., Ltd., Suzhou 215004, China
*
Author to whom correspondence should be addressed.
Received: 11 March 2019 / Revised: 8 April 2019 / Accepted: 11 April 2019 / Published: 15 April 2019
(This article belongs to the Special Issue Optical Materials for Sensing and Bioimaging: Advances and Challenges)
  |  
PDF [6020 KB, uploaded 15 April 2019]
  |  

Abstract

In this paper, we propose a fast sparse recovery algorithm based on the approximate l0 norm (FAL0), which is helpful in improving the practicability of the compressed sensing theory. We adopt a simple function that is continuous and differentiable to approximate the l0 norm. With the aim of minimizing the l0 norm, we derive a sparse recovery algorithm using the modified Newton method. In addition, we neglect the zero elements in the process of computing, which greatly reduces the amount of computation. In a computer simulation experiment, we test the image denoising and signal recovery performance of the different sparse recovery algorithms. The results show that the convergence rate of this method is faster, and it achieves nearly the same accuracy as other algorithms, improving the signal recovery efficiency under the same conditions. View Full-Text
Keywords: sparse recovery; compressed sensing; approximate l0 norm; modified Newton method sparse recovery; compressed sensing; approximate l0 norm; modified Newton method
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Jin, D.; Yang, Y.; Ge, T.; Wu, D. A Fast Sparse Recovery Algorithm for Compressed Sensing Using Approximate l0 Norm and Modified Newton Method. Materials 2019, 12, 1227.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Materials EISSN 1996-1944 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top