ABSTRACT Testing and detecting potential defects in works of art is usually required to cause minimal or no damage; industrial Radiography Testing (RT) is often the method of choice provided the images are of the required high quality and yield high defect detection sensitivity. Various digital image processing methods can be employed to achieve improved image quality and information extraction as required. The level and nature of the noise in the RT images is usually unknown. In addition to the different noises, the image quality is degraded by the blurring effect. In this study, a modified form of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) with quadratic regularization strategy was developed and applied to remove the blurring. The output of the application of the method to number RT images of five art objects was evaluated by industrial radiography and antiquities experts. It was confirmed that the applied method was efficient and improved image quality and defect detection.

中文翻译：

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FISTA 算法，用于通过背景模糊去除增强 X 光图像

摘要 艺术作品中的潜在缺陷的测试和检测通常需要造成最小或不造成损坏；工业射线照相测试 (RT) 通常是首选方法，前提是图像具有所需的高质量和高缺陷检测灵敏度。可以根据需要采用各种数字图像处理方法来实现改进的图像质量和信息提取。RT 图像中噪声的级别和性质通常是未知的。除了不同的噪声外，图像质量也会因模糊效果而下降。在这项研究中，开发了具有二次正则化策略的快速迭代收缩阈值算法 (FISTA) 的改进形式，并应用于消除模糊。工业射线照相和古物专家评估了应用该方法对五个艺术品的 RT 图像进行编号的输出。证实了所应用的方法是有效的并且提高了图像质量和缺陷检测。