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.

Cite

Text

Ancuti et al. "NTIRE 2018 Challenge on Image Dehazing: Methods and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00134

Markdown

[Ancuti et al. "NTIRE 2018 Challenge on Image Dehazing: Methods and Results." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/ancuti2018cvprw-ntire/) doi:10.1109/CVPRW.2018.00134

BibTeX

@inproceedings{ancuti2018cvprw-ntire,
  title     = {{NTIRE 2018 Challenge on Image Dehazing: Methods and Results}},
  author    = {Ancuti, Cosmin and Ancuti, Codruta O. and Timofte, Radu},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {891-901},
  doi       = {10.1109/CVPRW.2018.00134},
  url       = {https://mlanthology.org/cvprw/2018/ancuti2018cvprw-ntire/}
}