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 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 oas ba?adores 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, jolly rancher filled gummies 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.

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