NTIRE 2019 image dehazing challenge report
Ancuti, C. O.; Ancuti, C.; Timofte, R.; Gool, L. V.; Zhang, L.; Yang, M. H.; Guo, T.; Li, X.; Cherukuri, V.; Monga, V.; Jiang, H.; Yang, S.; Liu, Y.; Qu, X.; Wan, P.; Park, D.; Chun, S. Y.; Hong, M.; Huang, J.; Chen, Y.; Chen, S.; Wang, B.; Michelini, P. N.; Liu, H.; Zhu, D.; Liu, J.; Santra, S.; Mondal, R.; Chanda, B.; Morales, P.; Klinghoffer, T.; Quan, L. M.; Kim, Y. G.; Liang, X.; Li, R.; Pan, J.; Tang, J.; Purohit, K.; Suin, M.; Rajagopalan, A. N.; Schettini, R.; Bianco, S.; Piccoli, F.; Cusano, C.; Celona, L.; Hwang, S.; Ma, Y. S.; Byun, H.; Murala, S.; Dudhane, A.; Aulakh, H.; Zheng, T.; Zhang, T.; Qin, W.; Zhou, R.; Wang, S.; Tarel, J. P.; Wang, C.; Wu, J.
Date:
2021-08-21
Abstract:
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.
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