ABSTRACT Industrial radiography is considered as one of the most important nondestructive testing methods for different inspections. The radiography images often have a poor signal-to-noise ratio mainly because of the scattered X-rays. Image processing methods may be used to enhance the contrast of radiographs for better defect detection. In this study, outcomes from three total variations (TV) based methods were analyzed and compared. Implemented algorithms were ROF-TV, non-convex p-norm total variation (NCP-TV) and non-convex logarithm-based total variation (NCLog-TV). These TV-based methods have been implemented indirectly as high pass edge-enhancing filters. Based on qualitative operator perception results, the study has shown that the application of all three methods resulted in improved image contrast enabling enhanced image detail visualization. Subtle performance differences between the outputs from different algorithms were noted, however, especially around the edges of image features. Furthermore, it was found that all implemented algorithms have similarities in performance, generate approximately the same results and are suitable for weld inspection.

中文翻译：

---

三种基于全变的算法在增强工业射线照相图像对比度方面的性能

摘要 工业射线照相被认为是用于不同检查的最重要的无损检测方法之一。射线照相图像通常具有较差的信噪比，主要是由于散射的 X 射线。图像处理方法可用于增强射线照片的对比度，以便更好地检测缺陷。在这项研究中，分析和比较了三种基于总变异 (TV) 的方法的结果。实现的算法是 ROF-TV、非凸 p 范数总变异 (NCP-TV) 和基于非凸对数的总变异 (NCLog-TV)。这些基于 TV 的方法已间接实现为高通边缘增强滤波器。基于定性操作员感知结果，研究表明，所有三种方法的应用都可以提高图像对比度，从而增强图像细节的可视化。然而，注意到不同算法的输出之间存在细微的性能差异，尤其是在图像特征的边缘附近。此外，发现所有实现的算法在性能上具有相似性，产生大致相同的结果并且适用于焊缝检测。