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Deep Learning-based Resolution Enhancement in SAR Image for Automotive Radar Sensors

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dc.contributor.authorKang, Sung-Wook-
dc.contributor.authorCho, Hahng-Jun-
dc.contributor.authorLee, Hojung-
dc.contributor.authorLee, Seongwook-
dc.date.accessioned2024-01-24T05:31:56Z-
dc.date.available2024-01-24T05:31:56Z-
dc.date.issued2023-10-
dc.identifier.issn1930-0395-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71357-
dc.description.abstractForming high-resolution synthetic aperture radar (SAR) images requires large amounts of sampled data, which increases computation time and complexity. Therefore, in this paper, we propose a method to enhance the resolution of SAR images for automotive radar sensors using a generative adversarial network (GAN). The proposed GAN is an unsupervised image-to-image translation GAN based on a variational autoencoder and can form high-resolution SAR images from a small amount of sampled data. The SAR images formed by the proposed method are compared in terms of peak signal-to-noise ratio and structural similarity index measure for performance evaluation, and they are increased by 2.75% and 4.43%, respectively, compared to the existing low-resolution SAR images. © 2023 IEEE.-
dc.language영어-
dc.language.isoENG-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleDeep Learning-based Resolution Enhancement in SAR Image for Automotive Radar Sensors-
dc.typeArticle-
dc.identifier.doi10.1109/SENSORS56945.2023.10325124-
dc.identifier.bibliographicCitationProceedings of IEEE Sensors-
dc.description.isOpenAccessN-
dc.identifier.scopusid2-s2.0-85179764899-
dc.citation.titleProceedings of IEEE Sensors-
dc.type.docTypeConference paper-
dc.publisher.location미국-
dc.subject.keywordAuthorautomotive radar sensor-
dc.subject.keywordAuthorgenerative adversarial network-
dc.subject.keywordAuthorsynthetic aperture radar-
dc.description.journalRegisteredClassscopus-
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