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<br />nuclear industries. However, because of the physical limitations of neutron sources and collimators, the resulting<br />neutron radiographic images inevitably exhibit multiple distortions, including noise, geometric unsharpness,<br />and white spots. Furthermore, these distortions are particularly significant in compact neutron radiography systems<br />with low neutron fluxes. Therefore, in this study, we devised a multi-distortion suppression network that<br />employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images.<br />Real neutron radiographic image datasets with various types and levels of distortion were built for the first<br />time as multi-distortion suppression datasets. Thereafter, the coordinate attention mechanism was incorporated<br />into the backbone network to augment the capability of the proposed network to learn the abstract relationship<br />between ideally clear and degraded images. Extensive experiments were performed; the results show that the<br />proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve<br />state-of-the-art perceptual visual quality, thus demonstrating its application potential in neutron radiography.