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

IEEE Access

卷:8

Seung-Won Jung¹  Sung-Jea Ko¹  Hyo-Young Kim¹  Yong-Goo Shin²  Seung Park³ 

[1] Department of Electrical Engineering, Korea University, Seoul, South Korea;

[2] Division of Smart Interdisciplinary Engineering, Hannam University, Daejeon, South Korea;

[3] Medical AI Research Center, Samsung Medical Center, Seoul, South Korea;

关键词: Convolutional neural network;    high dynamic range;    tone mapping;    unsupervised learning;    X-ray imaging;   

DOI  :  10.1109/ACCESS.2020.3035086

来源: DOAJ

【 摘 要 】

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 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.

【 授权许可】

Unknown