王琦,崔莉莎,汪剑鸣,孙玉宽,王化祥.基于电磁层析成像的金属缺陷稀疏成像方法[J].仪器仪表学报,2017,38(9):2291-2398
基于电磁层析成像的金属缺陷稀疏成像方法
Defects detection based on electromagnetic tomography for sparse imaging method
  
DOI:
中文关键词:  金属缺陷  电磁层析成像  稀疏性  l1正则化算法  l2正则化算法
英文关键词:metal defects  electromagnetic tomography  sparsity  l1 regularization  l2 regularization
基金项目:国家自然科学基金(61373104,61402330,61405143,61601324)、天津市应用基础与前沿技术研究计划(15JCQNJC01500)项目资助
作者单位
王琦 1.天津工业大学电子与信息工程学院天津300387;2.天津市光电检测技术与系统重点实验室天津 300387 
崔莉莎 1.天津工业大学电子与信息工程学院天津300387;2.天津市光电检测技术与系统重点实验室天津 300387 
汪剑鸣 1.天津工业大学电子与信息工程学院天津300387;2.天津市光电检测技术与系统重点实验室天津 300387 
孙玉宽 1.天津工业大学电子与信息工程学院天津300387;2.天津市光电检测技术与系统重点实验室天津 300387 
王化祥 天津大学电气与自动化工程学院天津300072 
AuthorInstitution
Wang Qi 1. School of Electronics and Information Engineering Tianjin Polytechnic University, Tianjin 300387, China;2. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China 
Cui Lisha 1. School of Electronics and Information Engineering Tianjin Polytechnic University, Tianjin 300387, China;2. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China 
Wang Jianming 1. School of Electronics and Information Engineering Tianjin Polytechnic University, Tianjin 300387, China;2. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China 
Sun Yukuan 1. School of Electronics and Information Engineering Tianjin Polytechnic University, Tianjin 300387, China;2. Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China 
Wang Huaxiang School of Electrical Engineering and Automation Tianjin University,,Tianjin 300072,China 
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中文摘要:
      采用电磁层析成像(electromagnetic tomography, EMT)技术实现对金属缺陷的可视化,克服了传统的检测技术无法对缺陷进行可视化的不足。首先设计了一种新型的平面EMT传感器,其次根据缺陷分布的稀疏性,提出了l1正则化稀疏成像算法。该算法能够有效避免传统的l2正则化算法带来的过度光滑的问题,成像更加精确。最后为证明该算法相对于l2正则化算法的优越性,进行了仿真和实验。仿真和实验结果均表明l1正则化稀疏成像算法能够有效提高缺陷图像的重建质量和精度。
英文摘要:
      Electromagnetic tomography (EMT) technology is used to realize the visualization of metal defects, which overcomes the lack of visualization of traditional testing technology. Firstly, a new planar sensor is designed. Secondly, according to the sparsity of defect distribution, the l1 regularized sparse imaging algorithm is proposed. The l1 regularization algorithm effectively overcomes the excessive smooth problem associated with traditional l2 regularization algorithm, whose imaging results are more accurate. Finally, in order to further prove the superiority of the new algorithm compared with l2 regularization algorithm, the simulation and experiment are conducted. The results show that sparse imaging algorithm can effectively improve the quality and accuracy of the defects images.
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