scholarly journals Sparse Recovery Algorithm for Compressed Sensing Using Smoothed l0 Norm and Randomized Coordinate Descent

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 834
Author(s):  
Jin ◽  
Yang ◽  
Li ◽  
Liu
Keyword(s):  
Compressed Sensing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Sparse Recovery ◽  
Coordinate Descent ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Core Concepts

Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory. In this paper, we proposed a sparse recovery algorithm using a smoothed l0 norm and a randomized coordinate descent (RCD), then applied it to sparse signal recovery and image denoising. We adopted a new strategy to express the (P0) problem approximately and put forward a sparse recovery algorithm using RCD. In the computer simulation experiments, we compared the performance of this algorithm to other typical methods. The results show that our algorithm possesses higher precision in sparse signal recovery. Moreover, it achieves higher signal to noise ratio (SNR) and faster convergence speed in image denoising.

RBF-network based sparse signal recovery algorithm for compressed sensing reconstruction

2015 ◽  
Vol 63 ◽  
pp. 66-78 ◽  
Author(s):  
Vidya L. ◽  
Vivekanand V. ◽  
Shyamkumar U. ◽  
Deepak Mishra
Keyword(s):  
Compressed Sensing ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Rbf Network ◽  
Recovery Algorithm

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

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1227 ◽  
Author(s):  
Dingfei Jin ◽  
Yue Yang ◽  
Tao Ge ◽  
Daole Wu
Keyword(s):  
Compressed Sensing ◽  
Newton Method ◽  
Simulation Experiment ◽  
Sparse Recovery ◽  
Signal Recovery ◽  
Recovery Efficiency ◽  
Recovery Algorithm ◽  
Modified Newton Method ◽  
Computer Simulation Experiment ◽  
Recovery Algorithms

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.

scholarly journals Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing

2013 ◽  
Vol 9 (8) ◽  
pp. 798537 ◽  
Author(s):  
ZheTao Li ◽  
JingXiong Xie ◽  
DengBiao Tu ◽  
Young-June Choi
Keyword(s):  
Compressed Sensing ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery

scholarly journals High-Resolution ISAR Imaging Based on Improved Sparse Signal Recovery Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Junjie Feng ◽  
Yinan Sun ◽  
XiuXia Ji
Keyword(s):  
High Resolution ◽  
Optimization Problem ◽  
Signal Reconstruction ◽  
Variable Parameter ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Loop Structure ◽  
Recovery Algorithm ◽  
Isar Imaging

In order to solve the problem of high-resolution ISAR imaging under the condition of finite pulses, an improved smoothed L0 norm (SL0) sparse signal reconstruction ISAR imaging algorithm is proposed. Firstly, the ISAR imaging is transformed into the optimization problem of minimum L0 norm. Secondly, a single-loop structure is used instead of two loop layers in SL0 algorithm which increases the searching density of variable parameter to ensure the recovery accuracy. Finally, the compared step is added to ensure the optimization solution along the steepest descent gradient direction. The experimental results show that the proposed algorithm has better imaging effect.

scholarly journals A Fast and Robust Non-Sparse Signal Recovery Algorithm for Wearable ECG Telemonitoring Using ADMM-Based Block Sparse Bayesian Learning

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2021 ◽  
Author(s):  
Yunfei Cheng ◽  
Yalan Ye ◽  
Mengshu Hou ◽  
Wenwen He ◽  
Yunxia Li ◽  
...  
Keyword(s):  
Bayesian Learning ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Sparse Bayesian Learning ◽  
Recovery Algorithm
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