| 基于压缩传感理论的重构算法研究 |
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| 引用本文： | 张 涛,钟舜聪,朱志彬,等. 基于压缩传感理论的重构算法研究[J]. 机电工程, 2014, 0(6): 805-808,818 |
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| 作者姓名： | 张 涛 钟舜聪 朱志彬 等 |
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| 作者单位： | [1]福州大学机械工程及自动化学院无损检测实验室,福建福州350108 [2]福建省医疗器械和生物技术重点实验室,福建福州350002 [3]华东理工大学承压系统安全科学教育部重点实验室,上海200237 [4]福州大学石油化工学院,福建福州350108 [5]厦门市特种设备检验检测院,福建厦门361000 |
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| 基金项目： | 国家自然科学基金资助项目（51005077）;教育部高学校博士学科点科研基金资助项目（博导类,20133514110008）;国家卫生和计划生育委员会科研基金资助项目（WKJ-FJ-27）;福建省杰出青年基金资助项目（2011J06020）;福建省质量技术监督局科技资助项目（FJQ12013095、FJQ12012024）;福建省高等学校学科带头人培养计划资助项目（闽教人[-2013]71号） |
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| 摘 要： | 针对国内压缩传感理论(CS)尚处于起步以及理论研究阶段,为深入阐述该理论及对其实践应用性进行探索,将压缩传感理论从信号的稀疏表示、编码测量以及重构算法3个方面展开了较为详细的论述,并深入地阐述了重构算法中具有代表性的匹配追踪、正交匹配追踪、正则化正交匹配追踪算法。并进一步介绍了最小均方差线性估计(MMSE)算法,通过与常用重构算法的仿真对比,突出了MMSE算法在低采样率下的优越性。研究结果表明,该算法在实践中具有较好的应用潜力。
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| 关 键 词： | 压缩传感 稀疏性 信号处理 信号重构 重构算法 MMSE |
Reconstruction algorithm based on compressed sensing |
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| ZHANG Tao,ZHONG Shun-cong,ZHU Zhi-bin,FU Xi-bin. Reconstruction algorithm based on compressed sensing[J]. Mechanical & Electrical Engineering Magazine, 2014, 0(6): 805-808,818 |
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| Authors: | ZHANG Tao ZHONG Shun-cong ZHU Zhi-bin FU Xi-bin |
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| Affiliation: | 1. Laboratory of Non-destructive Testing ＆ Evaluation, School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China; 2. Fujian Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou 350002, China; 3. Key Laboratory of Safety Science of Pressurized System of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; 4. School of Chemical Engineering, Fuzhou University, Fuzhou 350108, China; 5. Xiamen Special Equipment Inspection Institute, Xiamen 361000, China) |
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| Abstract: | Aiming at introducing and using compressive sensing（CS）into practice, CS was discussed in detail from sparse representation, encoding measurement and reconstruction algorithms. The commonly used reconstruction algorithms, such as matching pursuit, orthogonal matching pursuit and the regularized orthogonal matching pursuit, were reviewed. The minimum mean square error （MMSE） linear estimatealgorithm, which showed better reconstruction quality under low sampling rate, was introduced as well. Through the image simulation, compared with commonly used reconstruction algorithms, the results indicate that MMSE shows great superiority and potential for real applications. |
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| Keywords: | compressive sensing sparsity signal processing signal reconstruction algorithms minimum mean square error（MMSE） |
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