This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/fog generator that imitates the real conditions of haze scenes. The evaluation consists from the comparison of the dehazed images with the ground truth images. The dehazing process was learnable through provided pairs of haze-free and hazy train images. There were ~ 270 registered participants and 23 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.
|Title of host publication||Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019|
|Publisher||IEEE Computer Society|
|Number of pages||13|
|Publication status||Published - 2019 Jun|
|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
|Name||IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops|
|Conference||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019|
|Period||19/6/16 → 19/6/20|
Bibliographical noteFunding Information:
We thank the NTIRE 2019 sponsors: Huawei Technologies Co. Ltd., NVIDIA Corp., Amazon.com, Inc., Sam-sung, MediaTek, Oppo and ETH Zurich (Computer Vision Lab). Part of this work was supported by research grant GNaC2018 - ARUT, no. 1361-01.02.2019, financed by Politehnica University of Timisoara. Part of this work was supported by 2020 European Union Research and Innovation Horizon 2020 under the grant agreement Marie Sklodowska-Curie No 712949 (TECNIOspring PLUS), as well as the Agency for the Competitiveness of the Company of the Generalitat de Catalunya - ACCIO: TECSPR17-1-0054.
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering