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

Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace, military, and nuclear industries. However, because of the physical limitations of neutron sources and collimators, the resulting neutron radiographic images inevitably exhibit multiple distortions, including noise, geometric unsharpness, and white spots. Furthermore, these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes. Therefore, in this study, we devised a multi-distortion suppression network that employs 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 first time as multi-distortion suppression datasets. Thereafter, the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images. Extensive experiments were performed; the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-the-art perceptual visual quality, thus demonstrating its application potential in neutron radiography.