Radiographic Image Enhancement Method For Complex Components based on Deep Learning Theory

作者：Huyue Cheng, Hongquan Jiang, Zhen Liu, Yonghong Wang, Deyan Yang, Zelin Zhi · 发表于：2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC) · 年份：2022 · DOI：10.1109/itoec53115.2022.9734477 · 被引用次数：3 · 研究领域：Non-Destructive Testing Techniques、Advanced X-ray and CT Imaging、Industrial Vision Systems and Defect Detection

The internal defects of complex components are usually detected by X-ray, and the detection images generally have the problems of large gray change and low contrast. The research on image enhancement method is of great significance to improve the accuracy of defect recognition. To overcome the problems of adjusting parameters and the influence of personnel experience in traditional enhancement methods, this paper proposes a radiographic image enhancement method based on deep learning theory. Firstly, according to the requirements of radiographic image enhancement, the target data set of radiographic image enhancement is constructed. Secondly, using the deep learning theory, an improved U-Net network image enhancement model is designed to realize image structure preservation and noise removal. Finally, the proposed method is illustrated and verified by the radiographic images of complex metal components.