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Open Access

A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

Rui Wang( )Jinglei ZhangSuli RenQingjuan Li
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
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In recent years, Compressed Sensing (CS) has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit (OMP) is fixed, thus limiting reconstruction precision. Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit (RIOMP) algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations. The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses. When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time.


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Tsinghua Science and Technology
Pages 71-79
Cite this article:
Wang R, Zhang J, Ren S, et al. A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing. Tsinghua Science and Technology, 2016, 21(1): 71-79.








Web of Science






Received: 12 September 2015
Revised: 13 October 2015
Accepted: 02 November 2015
Published: 04 February 2016
© The author(s) 2016