X-ray imaging is one of the most widely used security measures for maintaining airport and transportation security. Conventional X-ray imaging systems typically apply tone-mapping (TM) algorithms to visualize high-dynamic-range (HDR) X-ray images on a standard 8-bit display device. However, X-ray images obtained through traditional TM algorithms often suffer from halo artifacts or detail loss in interobject overlapping regions, which makes it difficult for an inspector to detect unsafe or hazardous objects.To alleviate these problems, this article proposes a deep learning-based TM method for X-ray inspection. The proposed method consists of two networks called detail-recovery network (DR-Net) and TM network (TM-Net). The goal of DR-Net is to restore the details in the input HDR image, whereas TM-Net aims to compress the dynamic range while preserving the restored details and preventing halo artifacts. Since there are no standard ground-truth images available for the TM of X-ray images, we propose a novel loss function for unsupervised learning of TM-Net. We also introduce a dataset synthesis technique using the Beer-Lambert law for supervised learning of DR-Net. Extensive experiments comparing the performance of our proposed method with state-of-the-art TM methods demonstrate that the proposed method not only achieves visually compelling results but also improves the quantitative performance measures such as FSITM and HDR-VDP-2.2.INDEX TERMS Convolutional neural network, high dynamic range, tone mapping, unsupervised learning, X-ray imaging. HYO-YOUNG KIM (Member, IEEE) received the B.S. degree in electrical engineering from Korea University, in 2013, where he is currently pursuing the Ph.D. degree. His current research interests include image processing, computer vision, and artificial intelligence. SEUNG PARK received the B.S. and Ph.D. degrees in electrical engineering from Korea University, Seoul, South Korea, in 2013 and 2020, respectively. He was a Research Professor with Korea University, in 2020. He currently joined the Samsung Medical Center as an AI Researcher. His research interests include X-ray imaging and medical image analysis.