NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study

Seungjun Nah; Sungyong Baik; Seokil Hong; Gyeongsik Moon; Sanghyun Son; Radu Timofte; Kyoung Mu Lee

doi:10.1109/CVPRW.2019.00251

NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study

Seungjun Nah
, Sungyong Baik
, Seokil Hong
, Gyeongsik Moon
, Sanghyun Son
, Radu Timofte
, Kyoung Mu Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

485 Citations (Scopus)

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 super-resolution, reaching compelling performance on our newly proposed REDS dataset.

Original language	English
Title of host publication	Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Publisher	IEEE Computer Society
Pages	1996-2005
Number of pages	10
ISBN (Electronic)	9781728125060
DOIs	https://doi.org/10.1109/CVPRW.2019.00251
Publication status	Published - 2019 Jun
Externally published	Yes
Event	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States Duration: 2019 Jun 16 → 2019 Jun 20

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2019-June
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Country/Territory	United States
City	Long Beach
Period	19/6/16 → 19/6/20

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1109/CVPRW.2019.00251

Cite this

Nah, S., Baik, S., Hong, S., Moon, G., Son, S., Timofte, R., & Lee, K. M. (2019). NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 1996-2005). Article 9025509 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2019.00251

NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study. / Nah, Seungjun; Baik, Sungyong; Hong, Seokil et al.
Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society, 2019. p. 1996-2005 9025509 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nah, S, Baik, S, Hong, S, Moon, G, Son, S, Timofte, R & Lee, KM 2019, NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study. in Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019., 9025509, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, IEEE Computer Society, pp. 1996-2005, 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019, Long Beach, United States, 19/6/16. https://doi.org/10.1109/CVPRW.2019.00251

Nah S, Baik S, Hong S, Moon G, Son S, Timofte R et al. NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society. 2019. p. 1996-2005. 9025509. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW.2019.00251

Nah, Seungjun ; Baik, Sungyong ; Hong, Seokil et al. / NTIRE 2019 challenge on video deblurring and super-resolution : Dataset and study. Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society, 2019. pp. 1996-2005 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

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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 super-resolution, reaching compelling performance on our newly proposed REDS dataset.",

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AB - 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 super-resolution, reaching compelling performance on our newly proposed REDS dataset.

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NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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