Reconstruction algorithm for block compressed sensing based on variation model

Jian CHEN; xiong SUKai; zhi YANGXiu; kui ZHENGMing; qun LINLi

doi:10.11959/j.issn.1000-436x.2016011

您当前的位置：

首页 >

文章列表页 >

Reconstruction algorithm for block compressed sensing based on variation model

Academic paper | 更新时间：2024-06-05

- Reconstruction algorithm for block compressed sensing based on variation model
- Journal on Communications Vol. 37, Issue 1, Pages: 100-109(2016)
- 作者机构：
  
  福州大学物理与信息工程学院，福建福州 350116
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61471124);The National Natural Science Foundation of China(61571129);The Natural Science Founda-tion of Fujian Province(2013J01234);The Natural Science Founda-tion of Fujian Province(2014J01234);The Natural Science Founda-tion of Fujian Province(2015J01251);The Major Technology Project of Fujian Prov-ince(2014HZ0003-3);The Education Department Project of Fujian Province(JA14065)
- DOI：10.11959/j.issn.1000-436x.2016011
  CLC： TN911.73
- Online First：2016-01，
  
  Published：25 January 2016
- 稿件说明：
移动端阅览
Jian CHEN, xiong SUKai, zhi YANGXiu, et al. Reconstruction algorithm for block compressed sensing based on variation model[J]. Journal on Communications, 2016, 37(1): 100-109.
DOI：

Jian CHEN, xiong SUKai, zhi YANGXiu, et al. Reconstruction algorithm for block compressed sensing based on variation model[J]. Journal on Communications, 2016, 37(1): 100-109. DOI： 10.11959/j.issn.1000-436x.2016011.

摘要

为了提高现有块压缩感知重构算法的性能，提出了基于全变分和混合变分模型的块压缩感知（简称BCS-TV和BCS-MV）算法。该方法以块为单位进行图像采样，以自然图像正则项的稀疏性为先验条件，通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM)，在整幅图像范围内逼近目标函数来重构原始图像。与以前基于一致性块采样的压缩感知工作对比，该算法的PSNR约提高1.5 dB

SSIM约提高0.05，运行速度较稳定，特别适合具有固定传输时延的多媒体数据处理场合。

Abstract

The algorithms for block compressed sensing based on total variation and mixed variation (abbreviated as BCS-TV and BCS-MV) models were proposed to improve the performance of current reconstruction algorithms for the block-based compressed sensing. In the measuring phase

an image was sampled block-by-block. In the recovering period

it took the sparse regularization of the natural image as a priori knowledge

and approached the target function within the whole image through the modified augmented Lagrange method and alternating direction method of multipliers (ALM-ADMM). The method proposed achieves average PSNR gain of 1.5 dB and SSIM gain of 0.05 at a more stable running speed

over the previous uniformly block-based compressed sensing. It is particularly suitable for the applications of the multimedia data processing with fixed transmission delay.

关键词

Keywords

references

CANDES E , ROMBERG J , TAO T . Robust uncertainty princip es:exact signal reconstruction from highly incomplete frequency infor-mation [J ] . IEEE Transactions on Information Theory , 2006 , 52 ( 2 ): 489 - 509 .

DONOHO D . Compressed sensing [J ] . IEEE Transactions on Informa-tion Theory , 2006 , 52 ( 4 ): 1289 - 1306 .

ELDAR Y C , KUTYNIOK G . Compressed Sensing: Theory and Ap-plications [M ] . USA : Cambridge University Press , 2012

LU G . Block compressed sensing of natural images [C ] // The 15th International Conference on Digital Signal Processing . Cardiff , c 2007 : 403 - 406 .

THONG T D , TRAC D T , LU G . Fast compressive sampling with structurally random matrices [C ] // The 2008 IEEE International Confe-rence on Acoustics, Speech and Signal Processing (ICASSP). Las Ve-gas, USA , c 2008 : 3369 - 3372 .

MUN S , FOWLER J . E.Block compressed sensing of images using directional transforms [C ] // Data Compression Conference (DCC). Snowbird, Utah , c 2010 : 547 .

FOWLER J E , MUN S , TRAMEL E W . Block-based compressed sensing of images and video [J ] . Foundations and Trends in Signal Processing , 2012 ( 4 ): 297 - 416 .

THONG T D , LU G , NAM H N , et al . Fast and efficient compressive sensing using structurally random matrices [J ] . IEEE Transactions on Signal Processing , 2012 , 60 ( 1 ): 139 - 154 .

ARMIN E , HAN L Y , CHRISTOPHER J R , et al . The restricted iso-metry property for random block diagonal matrices [J ] . Applied and Computational Harmonic Analysis , 2015 , 38 ( 1 ): 1 - 31 .

VAN T C , DINH K Q , JEON B . Edge-preserving block compressive sensing with projected landweber [C ] // The 20th International Confe-rence on Systems, Signals and Image Processing (IWSSIP). Bucharest, Romania , c 2013 : 71 - 74 .

FOUCART S , RAUHUT H . A Mathematical Introduction To Com-pressive Sensing [M ] . Springer New York Press , USA . 2013 .

LI C B . An Effcient Algorithm For Total Variation Regularization With Applications To The Single Pixel Camera And Compressive Sens-ing [D ] . Rice University , 2009 .

LI C B . Compressive Sensing For 3d Data Processing Tasks: Applica-tions, Models And Algorithms [D ] . Rice University , 2011 .

HE B S , TAO M , YUAN X M . A splitting method for separable con-vex programming [J ] . IMA Journal of Numerical Analysis , 2014 ,( 1 ): 1 - 33 .

HE B S , LIAO L Z , WANG X . Proximal-like contraction methods for monotone variational inequalities in a unified framework I: Effective quadruplet and primary methods [J ] . Computation Optimization Ap-plication , 2012 ,( 51 ): 649 - 679 .

HE B S , LIAO L Z , WANG X . Proximal-like contraction methods for monotone variational inequalities in a unified framework II: General methods and numerical experiments [J ] . Computation Optimization Application , 2012 ,( 51 ): 681 - 708 .

GU G Y , HE B S , YUAN X M . Customized proximal point a go-rithms for linearly constrained convex minimization a sad-dle-point problems: a unified approach [J ] . Computation Optimization Application , 2014 ,( 59 ): 135 - 161 .

Views

2838

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

ISAC constant modulus waveform design based on multi-target fair sensing

Improved transmit beamforming design based on ADMM for low probability of intercept of FDA-MIMO radar

Multi-source information transmission and classification algorithm for equipment based on compressed sensing

Image compressive sensing recovery based on weighted structure group sparse representation

Related Author

SHI Mingli

LI Xiaohui

LYU Siting

Pengcheng GONG

Yuntao WU

Xiaohu ZHAO

Gang WANG

Boming SONG

Related Institution

School of Teleommunications Engineering, Xidian University

School of Computer Science and Engineering, Wuhan Institute of Technology

Hubei Key Laboratory of Intelligent Robot

The National Joint Engineering Laboratory of Internet Applied Technology of Mines

IoT Perception Mine Research Center, China University of Mining and Technology

AI问答

⁰