Accelerate Compression Sensing Reconstruction Algorithms Using GPU

MIAO Zhuang; SUI Dong; LI Peng-yan; KE Jun

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Accelerate Compression Sensing Reconstruction Algorithms Using GPU

更新时间：2026-09-04

- Accelerate Compression Sensing Reconstruction Algorithms Using GPU
- Vol. 33, Issue 12, Pages: 125-129(2016)
- 作者机构：
  
  1. 昆明物理研究所机构微光夜视技术重点实验室,陕西,西安,710000
  2. 北京理工大学光电学院,北京,100081
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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苗壮, 隋冬, 李棚艳, 柯钧. 采用GPU加速的压缩感知图像恢复算法[J]. 微电子学与计算机, 2016, 33(12): 125-129.

MIAO Zhuang, SUI Dong, LI Peng-yan, et al. Accelerate Compression Sensing Reconstruction Algorithms Using GPU[J]. 2016, 33(12): 125-129.
苗壮, 隋冬, 李棚艳, 柯钧. 采用GPU加速的压缩感知图像恢复算法[J]. 微电子学与计算机, 2016, 33(12): 125-129. DOI：

MIAO Zhuang, SUI Dong, LI Peng-yan, et al. Accelerate Compression Sensing Reconstruction Algorithms Using GPU[J]. 2016, 33(12): 125-129. DOI：

摘要

压缩感知(Compressed Sensing

CS)的信号重构部分需要进行大数据量的计算

然而传统的CPU对大量的矢量计算并没有优势.为了解决这一问题

我们以CUDA作为并行计算架构

通过GPU-CPU并行编程技术

实现了三种快速高效的压缩感知图像恢复算法

包括正交匹配追踪OMP算法、两步阈值迭代TwIST算法和线性Bregman算法.

Abstract

The signal reconstruction in compressive sensing (CS) requires a large amount of data processing. However the traditional CPU has no advantage on vector calculation. To deal with this issue

we design a parallel computing architecture using CUDA. Based on GPU-CPU parallel programming technology

and we realize three fast and efficient CS image reconstruction algorithms

including OMP

TwIST

and linear Bregman algorithm.

关键词

Keywords

references

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Richard G, Baraniuk. Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-224. DOI: 118

Bioucas-Dias J, Figueiredo M. A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Trans Image Process, 2007, 16(12): 2992-3004. DOI: 2992

石光明, 刘丹华, 高大化, 等. 王良君压缩感知理论及其研究进展[J]. ACTA Electronica Sinica, 2009, 37(5): 1070-1081. DOI: 1070

StanleyOsher, Yu Mao, Bin Dong, et al. Fast linearized Bregman iteration for compressive sensing and sparse denoising[J]. Commun Math Sci, 2010, 8(1): 93-111. DOI: 93

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张锐. 基于压缩感知理论的图像压缩初步研究[J]. 电脑知识与技术, 2010, 6(4): 958-959. DOI: 958

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Nvidia Corporation. Nvidia cuda c best practice guide[R]. 2014.

Nvidia C. Cublas library. NVIDIA Corporation[R]. Santa Clara, California, 2008.

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