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dc.contributor.author Rim, Jaesung ko
dc.contributor.author Lee, Haeyun ko
dc.contributor.author Won, Jucheol ko
dc.contributor.author Cho, Sunghyun ko
dc.date.accessioned 2021-01-29T07:30:53Z -
dc.date.available 2021-01-29T07:30:53Z -
dc.date.created 2021-01-07 -
dc.date.issued 2020-08-25 -
dc.identifier.citation 16th European Conference on Computer Vision, ECCV 2020, pp.184 - 201 -
dc.identifier.isbn 9783030585945 -
dc.identifier.issn 0302-9743 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/12902 -
dc.description.abstract Numerous learning-based approaches to single image deblurring for camera and object motion blurs have recently been proposed. To generalize such approaches to real-world blurs, large datasets of real blurred images and their ground truth sharp images are essential. However, there are still no such datasets, thus all the existing approaches resort to synthetic ones, which leads to the failure of deblurring real-world images. In this work, we present a large-scale dataset of real-world blurred images and ground truth sharp images for learning and benchmarking single image deblurring methods. To collect our dataset, we build an image acquisition system to simultaneously capture geometrically aligned pairs of blurred and sharp images, and develop a postprocessing method to produce high-quality ground truth images. We analyze the effect of our postprocessing method and the performance of existing deblurring methods. Our analysis shows that our dataset significantly improves deblurring quality for real-world blurred images. © 2020, Springer Nature Switzerland AG. -
dc.language English -
dc.publisher Springer Science and Business Media Deutschland GmbH -
dc.title Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms -
dc.type Conference -
dc.identifier.doi 10.1007/978-3-030-58595-2_12 -
dc.identifier.scopusid 2-s2.0-85097435952 -
dc.type.local Article(Overseas) -
dc.type.rims CONF -
dc.description.journalClass 1 -
dc.contributor.nonIdAuthor Won, Jucheol -
dc.contributor.nonIdAuthor Cho, Sunghyun -
dc.identifier.citationStartPage 184 -
dc.identifier.citationEndPage 201 -
dc.identifier.citationTitle 16th European Conference on Computer Vision, ECCV 2020 -
dc.identifier.conferencecountry EI -
dc.identifier.conferencelocation Virtual Conference -
ETC2. Conference Papers

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