基于贝叶斯统计模型的金属缺陷电磁成像方法研究*
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中图分类号: TH701文献标识码: A国家标准学科分类代码: 4604010

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*基金项目:国家自然科学基金(61872269,61601324,61903273)、天津市自然科学基金(18JCYBJC85300)、天津市企业科级特派员项目 (18JCTPJC61600) 资助


Research on electromagnetic imaging of metal defects based on the Bayesian statistical model
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    摘要:

    摘要:提出了一种基于贝叶斯理论的电磁层析成像(EMT)图像重建算法。传统的正则化方法仅仅能获得单一电导率的近似估计值,提供的模型信息量有限。统计概率方法可以获得大量合理的模型参数估计值,根据缺陷分布的稀疏性,将求解电导率划分为一系列块状结构,使用稀疏贝叶斯学习框架,将电导率分布的稀疏先验信息和噪声信息等统计信息引入到EMT图像重建中,可以得到电导率分布全面的统计描述。为验证该算法的有效性,将新方法与共轭梯度算法、总变差正则化算法进行比较,并基于EMT实验系统进行了缺陷成像实验。仿真和实验结果表明,含有统计信息的稀疏贝叶斯算法与传统算法相比,图像误差降低20%,有效提高了重建图像质量与精度。

    Abstract:

    Abstract:This paper proposes an image reconstruction algorithm based on Bayesian theory for electromagnetic tomography (EMT). The traditional regularization algorithms for EMT reconstruction can only achieve a single estimation. Hence, the information provided by the model is limited. A large number of reasonable parameter estimations for the model can be obtained by statistical methods. According to the sparsity of defect distribution, the conductivity distribution is divided into a series of block structures. Under the framework of sparse Bayesian learning, statistical information, including the prior information of sparse representation for conductivity distribution and the noise information in the measurement data, is taken into account. In this way, the full statistical description of the conductivity distribution can be obtained. The conductivity distribution for the surface defects of metal part is reconstructed based on the sparse Bayesian algorithm. To further prove the feasibility of this algorithm, the reconstruction results of the new method is compared with those of the conjugate gradient method and the total variation regularization method. The defect imaging experiments are implemented based on the EMT system. Compared with traditional methods, both simulation and experimental results show that the relative errors of reconstructed images based on the Bayesian algorithm with statistical information can be reduced by 20%. The quality and accuracy of defects images are effectively improved.

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王琦,张静薇,张荣华,薛峰军,李秀艳.基于贝叶斯统计模型的金属缺陷电磁成像方法研究*[J].仪器仪表学报,2020,41(1):47-55

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  • 在线发布日期: 2022-01-11
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