[1]石曼曼,李 雷.一种改进的 OMP 算法及其在图像重构上的应用[J].计算机技术与发展,2018,28(02):94-97.[doi:10．3969/j．issn．1673－629X．2018．02．021]
　SHI Manman,LI Lei.An Improved Orthogonal Matching Pursuit Algorithm and Its Application in Image Ｒeconstruction[J].,2018,28(02):94-97.[doi:10．3969/j．issn．1673－629X．2018．02．021]
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一种改进的 OMP 算法及其在图像重构上的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年02期
页码:
94-97
栏目:
智能、算法、系统工程
出版日期:
2018-02-10

文章信息/Info

Title:
An Improved Orthogonal Matching Pursuit Algorithm and Its Application in Image Ｒeconstruction
文章编号:
1673－629X(2018)02－0094－04
作者:
石曼曼; 李 雷
南京邮电大学 理学院，江苏 南京 210023
Author(s):
SHI Man－man; LI Lei
School of Science，Nanjing University of Posts and Telecommunications，Nanjing 210023，China
关键词:
压缩感知; 正交匹配追踪; 压缩采样匹配追踪; 回溯; 图像重构
Keywords:
compressed sensing; orthogonal matching pursuit; compressive sampling matching pursuit; backtrack; image reconstruction
分类号:
TP301.6
DOI:
10．3969/j．issn．1673－629X．2018．02．021
文献标志码:
A
摘要:
为了提高正交匹配追踪(orthogonal matching pursuit，OMP)算法的重构精度，缩短重构时间，以此改善算法的重构性能，提出一种改进的双阈值分段迭代匹配追踪(dual threshold stage－wise iteration matching pursuit，DTSIMP)算法。该算法首先利用 OMP 算法迭代若干次，当残差小于第一阈值时引入回溯思想，利用压缩采样匹配追踪(compressed sampling matching pursuit，CoSaMP)算法继续迭代，并且将 OMP 算法迭代所得的残差和原子作为 CoSaMP 算法的初始输入值，当残差小于第二阈值时停止迭代。双阈值中，第一阈值控制 OMP 算法迭代次数，得到 CoSaMP 算法的最优初始输入;第二阈值控制信号的重构精度，以此实现精确快速地重构出稀疏信号。实验结果表明，对于一维的随机高斯信号，该算法重构误差小，重构时间少，并且在不同稀疏度和观测值下，算法重构成功率均高于原算法;对于二维图像信号，该算法重构时间少，重构效果好。
Abstract:
In order to improve the reconstruction accuracy and performance of orthogonal matching pursuit (OMP) and shorten its reconstruction time，we propose an improved dual threshold stage－wise iteration matching pursuit (DTSIMP)．It first uses the OMP for iteration until residual error is less than the first threshold value，then the idea of backtrack is added into the algorithm，which we take the compressed sampling matching pursuit (CoSaMP) for iteration again．The residual and atoms obtained from the OMP iterated are used as the input of the second step．Iteration stops when residual error is also less than the second preset threshold value．The first threshold controls the number of iterations of the OMP to get the optimal initial input of the CoSaMP，and the second threshold determines reconstruction accuracy of signal，so as to reconstruct sparse signals quickly and accurately．The experiment shows that the proposed algorithm is of little reconstruction error and reconstruction time with better reconstruction success rate for one－dimensional Gauss random signal when compared with OMP and Co-
SaMP．For two－dimensional image signal，it has the advantages of fast reconstruction time and excellent reconstruction effects．

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更新日期/Last Update: 2018-03-29