articleJun 1, 2019Closed access

NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study

ETH Zurich

Indexed incrossref

Abstract

This paper introduces a novel large dataset for video deblurring, video super-resolution and studies the state-of-the-art as emerged from the NTIRE 2019 video restoration challenges. The video deblurring and video super-resolution challenges are each the first challenge of its kind, with 4 competitions, hundreds of participants and tens of proposed solutions. Our newly collected REalistic and Diverse Scenes dataset (REDS) was employed by the challenges. In our study, we compare the solutions from the challenges to a set of representative methods from the literature and evaluate them on our proposed REDS dataset. We find that the NTIRE 2019 challenges push the state-of-the-art in video deblurring and…

Citation impact

523
total citations
FWCI
21.74
Percentile
100%
References
52
Citations per year

Authors

7

Topics & keywords

Keywords
  • Deblurring
  • Computer science
  • Artificial intelligence
  • Set (abstract data type)
  • State (computer science)
  • Computer vision
  • Superresolution
  • Resolution (logic)
UN Sustainable Development Goals
  • Sustainable cities and communities
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