A model-based regularized inverse method for ultrasonic B-scan image reconstruction
A model-based regularized inverse method for ultrasonic B-scan image reconstruction
复制标题
基于模型的超声B扫描图像重建正则逆方法
DOI：
10.1088/0957-0233/26/10/105401
复制
发表时间：
2015-10-01
影响因子：
2.4
通讯作者：
Yang, Keji
中科院分区：
文献类型：
--
作者：
Wu, Haiteng;Chen, Jian;Yang, Keji
超声B超成像受无损检测中声衍射和电效应的影响，导致缺陷的横向和时间分辨率不高。最小均方误差(MMSE)方法可以通过反演线性成像模型来提高分辨率，该模型考虑了声衍射和电学效应，并且比合成孔径聚焦技术(SAFT)获得了更高的分辨率。然而，由于缺陷的假设高斯分布，其计算效率和分辨率改善都不尽如人意。针对这些问题，提出了一种基于模型的正则化逆超声图像重建方法。该方法利用无损检测中缺陷分布稀疏的特点，构造了一个由ℓ1范数和ℓ2范数组成的逆目标函数，并采用可分离近似稀疏重构(Sparsa)算法得到最优解。通过对两根直径为0.3 mm的钢丝进行仿真和实验，对该方法的性能进行了评估。结果表明，该方法同时提高了横向和时间分辨率，具有较高的计算效率。
Ultrasonic B-scan imaging is affected by the acoustic diffraction and electrical effects in nondestructive testing (NDT), resulting in insufficient lateral and temporal resolution for defect characterization. The minimum mean squared error (MMSE) method can improve the resolution by inversing a linear imaging model, which takes the acoustic diffraction and electrical effects into account, and achieve higher resolution than the synthetic aperture focusing technique (SAFT). However, its computation efficiency and resolution improvement are unsatisfactory due to the hypothetical Gaussian distribution of defects. To overcome these problems, a model-based regularized inverse method for ultrasonic B-scan image reconstruction is proposed. Benefitting from the sparse distribution of defects in NDT applications, the proposed method formulates an inverse objective function composed of ℓ1 ?>-norm as well as ℓ2 ?>-norm, and the sparse reconstruction by a separable approximation (SpaRSA) algorithm is adopted to obtain the optimal solution. The performance of the proposed method is evaluated by B-scan imaging of two 0.3 mm steel wires conducted both in simulation and experiment. The results verify that the proposed method improves the lateral and temporal resolution simultaneously with high computation efficiency.