NTIRE 2018 challenge on image dehazing: Methods and results

Cosmin Ancuti; Codruta O. Ancuti; Radu Timofte; Luc Van Gool; Lei Zhang; Ming Hsuan Yang

doi:10.1109/CVPRW.2018.00134

NTIRE 2018 challenge on image dehazing: Methods and results

Cosmin Ancuti, Codruta O. Ancuti, Radu Timofte, Luc Van Gool, Lei Zhang, Ming Hsuan Yang

College of Computing

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

113 Citations (Scopus)

Abstract

This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had ~ 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.

Original language	English
Title of host publication	Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
Publisher	IEEE Computer Society
Pages	1004-1014
Number of pages	11
ISBN (Electronic)	9781538661000
DOIs	https://doi.org/10.1109/CVPRW.2018.00134
Publication status	Published - 2018 Dec 13
Event	31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States Duration: 2018 Jun 18 → 2018 Jun 22

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2018-June
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Other

Other	31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
Country/Territory	United States
City	Salt Lake City
Period	18/6/18 → 18/6/22

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1109/CVPRW.2018.00134

Cite this

Ancuti, C., Ancuti, C. O., Timofte, R., Van Gool, L., Zhang, L., & Yang, M. H. (2018). NTIRE 2018 challenge on image dehazing: Methods and results. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 (pp. 1004-1014). Article 8575287 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2018-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2018.00134

@inproceedings{1d378b170b084578a19165bd82fc188c,

title = "NTIRE 2018 challenge on image dehazing: Methods and results",

abstract = "This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had ~ 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.",

author = "Cosmin Ancuti and Ancuti, {Codruta O.} and Radu Timofte and {Van Gool}, Luc and Lei Zhang and Yang, {Ming Hsuan}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 ; Conference date: 18-06-2018 Through 22-06-2018",

year = "2018",

month = dec,

day = "13",

doi = "10.1109/CVPRW.2018.00134",

language = "English",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

publisher = "IEEE Computer Society",

pages = "1004--1014",

booktitle = "Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018",

address = "United States",

}

Ancuti, C, Ancuti, CO, Timofte, R, Van Gool, L, Zhang, L & Yang, MH 2018, NTIRE 2018 challenge on image dehazing: Methods and results. in Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018., 8575287, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018-June, IEEE Computer Society, pp. 1004-1014, 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018, Salt Lake City, United States, 18/6/18. https://doi.org/10.1109/CVPRW.2018.00134

NTIRE 2018 challenge on image dehazing: Methods and results. / Ancuti, Cosmin; Ancuti, Codruta O.; Timofte, Radu et al.
Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. IEEE Computer Society, 2018. p. 1004-1014 8575287 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2018-June).

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

TY - GEN

T1 - NTIRE 2018 challenge on image dehazing

T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018

AU - Ancuti, Cosmin

AU - Ancuti, Codruta O.

AU - Timofte, Radu

AU - Van Gool, Luc

AU - Zhang, Lei

AU - Yang, Ming Hsuan

PY - 2018/12/13

Y1 - 2018/12/13

N2 - This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had ~ 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.

AB - This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had ~ 120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.

UR - http://www.scopus.com/inward/record.url?scp=85058997308&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058997308&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2018.00134

DO - 10.1109/CVPRW.2018.00134

M3 - Conference contribution

AN - SCOPUS:85058997308

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

SP - 1004

EP - 1014

BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018

PB - IEEE Computer Society

Y2 - 18 June 2018 through 22 June 2018

ER -

Ancuti C, Ancuti CO, Timofte R, Van Gool L, Zhang L, Yang MH. NTIRE 2018 challenge on image dehazing: Methods and results. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018. IEEE Computer Society. 2018. p. 1004-1014. 8575287. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW.2018.00134

NTIRE 2018 challenge on image dehazing: Methods and results

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this