摘要：X-ray inspection is the most intuitive approach for the non-destructive testing (NDT) of pipeline weld defects to avoid pipeline safety accidents. However, identifying pipeline weld defects in dark X-ray images is difficult due to low greyscale values. This paper proposed a weakly supervised network for denoising and enhancing low-light pipeline weld X-ray images. First, a semi-supervised network based on an improved Retinex-Net which implemented by self-paced learning was proposed to enhance illumination, yielding more natural X-ray images without artifacts, distortion, and overexposure. A new denoising network constrained by the X-ray images themselves was designed to achieve denoising while preserving the image detail. Qualitative comparison and quantitative analysis indicated that the proposed method outperformed other industrial image enhancement methods used for pipeline weld detection in terms of both subjective visual effects and objective metric values. ? 2024 Elsevier Ltd

ISSN号：0963-8695

卷、期、页：卷143

发表日期：2024-04-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：NDT and E International

通讯作者：钱伟超,任庆滢

第一作者：董绍华,廖晨玲

论文类型：期刊论文

论文概要：钱伟超,董绍华,廖晨玲,任庆滢，Image enhancement method for low-light pipeline weld X-ray radiographs based on weakly supervised deep learning，NDT and E International，2024，卷143

论文题目：Image enhancement method for low-light pipeline weld X-ray radiographs based on weakly supervised deep learning