| 双阈值正交匹配追踪算法 |
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| 引用本文： | 刘馨月,赵志刚,吕慧显,王福驰,解昊.双阈值正交匹配追踪算法[J].计算机科学,2017,44(Z6):212-215, 228. |
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| 作者姓名： | 刘馨月 赵志刚 吕慧显 王福驰 解昊 |
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| 作者单位： | 青岛大学计算机科学技术学院 青岛 266071,青岛大学计算机科学技术学院 青岛 266071,青岛大学自动化与电气工程学院 青岛266071,青岛大学计算机科学技术学院 青岛 266071,青岛大学计算机科学技术学院 青岛 266071 |
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| 基金项目： | 本文受山东省科学技术发展计划(2012YD01058)资助 |
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| 摘 要： | 压缩感知理论(CS)中的重构算法是压缩感知理论的重要组成部分。在稀疏度未知的情况下,一些重构算法表现不佳。针对该问题,提出一种基于双阈值的正交匹配追踪算法。通过对所选原子的两次筛选,能够在稀疏度未知的情况下,高效率、高质量地重构信号。与同类算法相比,所提算法能够很好地重构信号,重构精度较高,运行速度较快。
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| 关 键 词： | 压缩感知 重构算法 弱选择 阈值 稀疏度 |
Double Threshold Orthogonal Matching Pursuit Algorithm |
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| LIU Xin-yue,ZHAO Zhi-gang,LV Hui-xian,WANG Fu-chi and XIE Hao.Double Threshold Orthogonal Matching Pursuit Algorithm[J].Computer Science,2017,44(Z6):212-215, 228. |
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| Authors: | LIU Xin-yue ZHAO Zhi-gang LV Hui-xian WANG Fu-chi and XIE Hao |
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| Affiliation: | College of Computer Science and Technology,Qingdao University,Qingdao 266071,China,College of Computer Science and Technology,Qingdao University,Qingdao 266071,China,College of Automation and Electrical Engineering,Qingdao University,Qingdao 266071,China,College of Computer Science and Technology,Qingdao University,Qingdao 266071,China and College of Computer Science and Technology,Qingdao University,Qingdao 266071,China |
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| Abstract: | The reconstruction algorithm in theory of compressed sensing (CS) is an important part of compression perception theory.Under the unknown condition of the sparse degree,some reconstruction algorithms perform poorly.To solve this problem,a kind of orthogonal matching pursuit algorithm based on double threshold was put forward.Under the unknown condition of the sparse degree,the twice screening for the selected atoms can have high efficiency and high quality reconstruction image signal.The proposed algorithm can effectively reconstruct signals through experimental comparison with other algorithms.The proposed algorithm in this paper has higher reconstruction precision and has shorter running time. |
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| Keywords: | Compressed sensing Reconstruction algorithm Weak choice Threshold Sparse |
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