Detail Restoration and Tone Mapping Networks for X-Ray Security Inspection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Hyo-Young | - |
dc.contributor.author | Park, Seung | - |
dc.contributor.author | Shin, Yong-Goo | - |
dc.contributor.author | Jung, Seung-Won | - |
dc.contributor.author | Ko, Sung-Jea | - |
dc.date.accessioned | 2021-08-31T16:16:12Z | - |
dc.date.available | 2021-08-31T16:16:12Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/59076 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | QUALITY ASSESSMENT | - |
dc.subject | ALGORITHMS | - |
dc.subject | REPRODUCTION | - |
dc.subject | ENHANCEMENT | - |
dc.subject | VISIBILITY | - |
dc.subject | MODEL | - |
dc.title | Detail Restoration and Tone Mapping Networks for X-Ray Security Inspection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Seung-Won | - |
dc.contributor.affiliatedAuthor | Ko, Sung-Jea | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3035086 | - |
dc.identifier.scopusid | 2-s2.0-85102813779 | - |
dc.identifier.wosid | 000590454600001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.197473 - 197483 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.citation.startPage | 197473 | - |
dc.citation.endPage | 197483 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | QUALITY ASSESSMENT | - |
dc.subject.keywordPlus | ALGORITHMS | - |
dc.subject.keywordPlus | REPRODUCTION | - |
dc.subject.keywordPlus | ENHANCEMENT | - |
dc.subject.keywordPlus | VISIBILITY | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | high dynamic range | - |
dc.subject.keywordAuthor | tone mapping | - |
dc.subject.keywordAuthor | unsupervised learning | - |
dc.subject.keywordAuthor | X-ray imaging | - |
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