NTIRE 2019 Challenge on Video Deblurring: Methods and Results

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dc.contributor.authorKim, Munchurlko
dc.contributor.authorSim, Hyeonjunko
dc.date.accessioned2019-11-28T08:26:30Z-
dc.date.available2019-11-28T08:26:30Z-
dc.date.created2019-11-25-
dc.date.issued2019-06-17-
dc.identifier.citationComputer Vision and Pattern Recognition Workshops (CVPRW 2019), pp.1 - 11-
dc.identifier.urihttp://hdl.handle.net/10203/268692-
dc.description.abstractThis paper reviews the first NTIRE challenge on video deblurring (restoration of rich details and high frequency components from blurred video frames) with focus on the proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed dynamic motion blurs while Track 2 had additional MPEG video compression artifacts. Each competition had 109 and 93 registered participants. Total 13 teams competed in the final testing phase. They gauge the state-of-the-art in video deblurring problem.-
dc.languageEnglish-
dc.publisherComputer Vision Foundation-
dc.titleNTIRE 2019 Challenge on Video Deblurring: Methods and Results-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.beginningpage1-
dc.citation.endingpage11-
dc.citation.publicationnameComputer Vision and Pattern Recognition Workshops (CVPRW 2019)-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationLong Beach, California-
dc.contributor.localauthorKim, Munchurl-
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EE-Conference Papers(학술회의논문)
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