Detail Restoration and Tone Mapping Networks for X-Ray Security Inspection
- Authors
- Kim, Hyo-Young; Park, Seung; Shin, Yong-Goo; Jung, Seung-Won; Ko, Sung-Jea
- Issue Date
- 2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Convolutional neural network; high dynamic range; tone mapping; unsupervised learning; X-ray imaging
- Citation
- IEEE ACCESS, v.8, pp.197473 - 197483
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 197473
- End Page
- 197483
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/59076
- DOI
- 10.1109/ACCESS.2020.3035086
- ISSN
- 2169-3536
- Abstract
- 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.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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