NTIRE 2018 Challenge on Image Dehazing: Methods and Results

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dc.contributor.authorKim, Munchurlko
dc.contributor.authorKi, Sehwanko
dc.contributor.authorSIM, HYEONJUNko
dc.contributor.authorChoi, Jae Seokko
dc.contributor.authorKim, Soo Yeko
dc.contributor.authorSeo, Soominko
dc.contributor.authorKim, Saehunko
dc.date.accessioned2018-12-20T02:13:08Z-
dc.date.available2018-12-20T02:13:08Z-
dc.date.created2018-12-01-
dc.date.created2018-12-01-
dc.date.created2018-12-01-
dc.date.issued2018-06-18-
dc.identifier.citationIEEE Computer Vision and Pattern Recognition Workshops (CVPRW), pp.965 - 976-
dc.identifier.urihttp://hdl.handle.net/10203/247465-
dc.description.abstractThis paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.-
dc.languageEnglish-
dc.publisherIEEE Computer Society and the Computer Vision Foundation (CVF)-
dc.titleNTIRE 2018 Challenge on Image Dehazing: Methods and Results-
dc.typeConference-
dc.identifier.wosid000457636800127-
dc.identifier.scopusid2-s2.0-85058997308-
dc.type.rimsCONF-
dc.citation.beginningpage965-
dc.citation.endingpage976-
dc.citation.publicationnameIEEE Computer Vision and Pattern Recognition Workshops (CVPRW)-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationSalt Lake City, UT, USA-
dc.identifier.doi10.1109/CVPRW.2018.00134-
dc.contributor.localauthorKim, Munchurl-
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