Multi-distortion suppression for neutron radiographic images based on generative adversarial network

分类： 物理学 >> 核物理学 提交时间： 2024-03-08

Cheng-Bo Meng Wang-Wei Zhu Zhen Zhang Zi-Tong Wang Chen-Yi Zhao Shuang Qiao Tian Zhang

摘要： Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace, military, andnuclear industries. However, because of the physical limitations of neutron sources and collimators, the resultingneutron radiographic images inevitably exhibit multiple distortions, including noise, geometric unsharpness,and white spots. Furthermore, these distortions are particularly significant in compact neutron radiography systemswith low neutron fluxes. Therefore, in this study, we devised a multi-distortion suppression network thatemploys a modified generative adversarial network to improve the quality of degraded neutron radiographic images.Real neutron radiographic image datasets with various types and levels of distortion were built for the firsttime as multi-distortion suppression datasets. Thereafter, the coordinate attention mechanism was incorporatedinto the backbone network to augment the capability of the proposed network to learn the abstract relationshipbetween ideally clear and degraded images. Extensive experiments were performed; the results show that theproposed method can effectively suppress multiple distortions in real neutron radiographic images and achievestate-of-the-art perceptual visual quality, thus demonstrating its application potential in neutron radiography.

通过

 点击量 513  下载量 139  评论