Detail Restoration and Tone Mapping Networks for X-Ray Security Inspection

Detail Restoration and Tone Mapping Networks for X-Ray Security Inspection

Blog Article

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 dea eyewear artifacts or detail loss in inter-object 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 phyre vapes 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.