Eddy current pulsed thermography (ECPT) is widely used in the nondestructive testing (NDT) of metal surface defects. Because defect information is sometimes affected by the edge effect, it is necessary to segment the ECPT image sequence, that is, separate the defected part from the background, to improve the detection effect. Tensor robust principal component analysis has been widely used in many image segmentation fields, such as medical imaging, face recognition, and so on, which also includes NDT, but it faces the problems of high time cost, overfitting, and insufficient physical interpretability in industrial applications. In this article, a new variable, global Moran’s index, is introduced to tensor decomposition for ECPT image sequences processing since the defect signal always gathers around the defects and shows spatial cohesion. The proposed method, named Moran’s index-based tensor decomposition (MITD), can significantly reduce the iterations, by up to 97%, and remove the influence of background noise. To demonstrate the performance of MITD, several experiments are carried out on three artificial samples and three natural samples from a nuclear power plant. Furthermore, a detailed comparison is drawn between MITD and other existing methods. The experimental results show that MITD not only reduces the time cost but also improves the image contrast.

中文翻译：

---

用于涡流脉冲热成像序列处理的基于莫兰指数的张量分解


涡流脉冲热成像（ECPT）广泛应用于金属表面缺陷的无损检测（NDT）。由于缺陷信息有时会受到边缘效应的影响，因此需要对ECPT图像序列进行分割，即将缺陷部分与背景分离，以提高检测效果。张量鲁棒主成分分析已广泛应用于许多图像分割领域，如医学成像、人脸识别等，其中也包括NDT，但在工业中面临着时间成本高、过拟合、物理可解释性不足等问题应用程序。在本文中，由于缺陷信号总是聚集在缺陷周围并表现出空间凝聚力，因此将一个新变量全局莫兰指数引入到 ECPT 图像序列处理的张量分解中。所提出的方法称为莫兰基于索引的张量分解（MITD），可以显着减少迭代次数，高达 97％，并消除背景噪声的影响。为了证明 MITD 的性能，对来自核电站的三个人造样本和三个天然样本进行了多项实验。此外，还对 MITD 和其他现有方法进行了详细比较。实验结果表明，MITD不仅降低了时间成本，而且提高了图像对比度。