一种均匀化稀疏表示的图像压缩感知算法

王浩; 梁煜; 张为

doi:10.7652/xjtuxb201902018

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一种均匀化稀疏表示的图像压缩感知算法

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- 一种均匀化稀疏表示的图像压缩感知算法
- A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation
- 西安交通大学学报 2019年53卷第2期页码：136-141
- 作者机构：
  
  天津大学微电子学院,天津,300072
- 作者简介：
- 基金信息：
  
  : 2018-05-16。作者简介: 王浩(1993—),男,硕士生;张为(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61474080)
- DOI：10.7652/xjtuxb201902018
  中图分类号： TP391.41
- 网络首发：2019-02-10，
  
  纸质出版：2019
- 稿件说明：
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王浩, 梁煜, 张为. 一种均匀化稀疏表示的图像压缩感知算法[J]. 西安交通大学学报, 2019,53(2):136-141.

A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation[J]. 2019, 53(2): 136-141.
王浩, 梁煜, 张为. 一种均匀化稀疏表示的图像压缩感知算法[J]. 西安交通大学学报, 2019,53(2):136-141. DOI： 10.7652/xjtuxb201902018.

A Compressed Sensing Algorithm of Images with Homogenized Sparse Representation[J]. 2019, 53(2): 136-141. DOI： 10.7652/xjtuxb201902018.

摘要

针对基于Reed-Solomon编码的压缩感知(RSCS)算法在采样过程中遇到的向量稀疏度阈值过大的问题

提出了一种均匀化稀疏表示的RSCS(H-RSCS)算法。首先

对待观测图像做多级离散小波变换(DWT)得到稀疏矩阵

然后按照其子带频率的高低顺序

将每个子带的系数重新按行排布成一个行数值固定的矩阵

矩阵中每一列数据组成一个新的待观测向量

最后采用奇偶校验矩阵对上述均匀化的稀疏矩阵进行观测

并通过译码算法实现图像重构。仿真实验结果表明:与4种经典的贪婪追踪类算法相比

所提出的H-RSCS算法对图像的重构效果更好

实用性更强; 当采样率为50%时

H-RSCS算法将重构图像的峰值信噪比提高了约9.5 dB

比正交匹配追踪算法多提高了约5.1 dB。

Abstract

A novel homogenized RSCS(H-RSCS)algorithm is proposed to deal with the problem of large vector sparseness threshold that exists in compressed sensing algorithm based on Reed-Solomon coding(RSCS). A multi-level discrete wavelet transform is applied to an observed image to obtain a sparse matrix. Then coefficients of each sub-band are rearranged into a matrix with fixed row values according to the order of the sub-band frequencies

and data in each column constitute a new vector to be observed. Finally

the above-mentioned homogenized sparse matrix is observed by the parity checking matrix

and image reconstruction is realized by the decoding algorithm. A comparison with the classic greedy tracking algorithm shows that the proposed H-RSCS algorithm obtains a more accurate reconstructed image. Experimental results show that when the sampling rate is 0.5

the H-RSCS algorithm increases the ratio of peak signal to noise of the reconstructed image by about 9.5 dB

which is 5.1 dB higher than that of the orthogonal matching pursuit algorithm.

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references

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

蔡体健, 樊晓平. 贪婪追踪系列算法的分析与优化 [J]. 小型微型计算机系统, 2014, 35(5): 1116-1119.

CAI Tijian, FAN Xiaoping. Analysis and optimization of greedy pursuit algorithms [J]. Journal of Chinese Computer Systems, 2014, 35(5): 1116-1119.

CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit [J]. SIAM Review, 2001, 43(1): 129-159.

PARVARESH F, HASSIBI B. Explicit measurements with almost optimal thresholds for compressed sensing [C]∥IEEE International Conference on Acoustics. Piscataway, NJ, USA: IEEE, 2008: 3853-3856.

MOHAMED M, RIZKALLA S, ZOERLEIN H, et al. Deterministic compressed sensing with power decoding for complex Reed-Solomon codes [C]∥10th International ITG Conference on Systems, Communications and Coding. Hamburg, Germany: VDE, 2015: 1-6.

SCHNIER T, BOCKELMANN C, DEKORSY A. RSCS: minimum measurement MMV deterministic compressed sensing based on complex Reed-Solomon coding [C]∥Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers. Piscataway, NJ, USA: IEEE, 2015: 483-487.

SCHNIER T, BOCKELMANN C, DEKORSY A. Minimum measurement deterministic compressed sensing based on complex Reed-Solomon decoding [C]∥2016 24th European Signal Processing Conference. Barcelona, Spain: EURASIP, 2016: 359-363.

MOHAMED M H, ZÖRLEIN H, BOSSERT M. Recursive enhancement of intrinsic soft information for complex Reed-Solomon codes [C]∥2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing. Piscataway, NJ, USA: IEEE, 2016: 1-5.

LIANG Z, ZHANG W. Efficient Berlekamp-Massey algorithm and architecture for Reed-Solomon decoder [J]. Journal of Signal Processing Systems, 2016, 86(1): 1-15.

侯兴松, 张兰. 方向提升小波变换域稀疏滤波的自然图像贝叶斯压缩感知 [J]. 西安交通大学学报, 2014, 48(10): 15-21.

HOU Xingsong, ZHANG Lan. A Bayesian compressive sensing algorithm for natural images based on sparse filtering in directional lifting wavelet transform domain [J]. Journal of Xi'an Jiaotong University, 2014, 48(10): 15-21.

LIU Y, LIANG Z, WANG Y, et al. Area-efficient Reed-Solomon decoder using recursive Berlekamp-Massey architecture for optical communication systems [J]. IEEE Communications Letters, 2017, 21(11): 2348-2351.

ROTH R M. Introduction to coding theory [M]. New York: Cambridge University Press, 2006: 148-150.

MASSEY J L. Shift-register synthesis and BCH decoding [J]. IEEE Transactions on Information Theory, 2003, 15(1): 122-127.

REED I S, SOLOMON G. Polynomial codes over certain finite fields [J]. Journal of the Society for Industrial Applied Mathematics, 1960, 8(2): 300-304.

王令宇, 张为, 刘艳艳. 一种RS码突发错误译码算法的改进算法 [J]. 南开大学学报(自然科学版), 2018, 50(1)50-53.

WANG Lingyu, ZHANG Wei, LIU Yanyan. A modified RS decoding algorithm for burst error [J]. Journal of Nankai University(Natural Science), 2018, 50(1)50-53.

WU Y. Novel burst error correction algorithms for Reed-Solomon codes [J]. IEEE Transactions on Information Theory, 2012, 58(2): 519-529.

TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.

DAI W, MILENKOVIC O. Subspace pursuit for compressive sensing signal reconstruction [J]. IEEE Transactions on Information Theory, 2009, 55(5): 2230-2249.

吕伟杰, 张飞, 胡晨辉. 基于双阈值的压缩采样匹配追踪改进算法 [J]. 控制与决策, 2017, 32(8): 1528-1532.

LÜ Weijie, ZHANG Fei, HU Chenhui. Modified compressive sampling matching pursuit algorithm based on double threshold [J]. Control and Decision, 2017, 32(8): 1528-1532.

MOHIMANI H, BABAIE-ZADEH M, JUTTEN C. A fast approach for overcomplete sparse decomposition based on smoothed l0, norm [J]. IEEE Transactions on Signal Processing, 2009, 57(1): 289-301.

LEITNER S, WANG H, TRAGOUDSA S. Design of scalable hardware-efficient compressive sensing image sensors [J]. IEEE Sensors Journal, 2017, 18(2): 641-651.

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