학술논문
NTIRE 2018 Challenge on Image Dehazing: Methods and Results
Document Type
Conference
Author
Ancuti, C.; Ancuti, C.O.; Timofte, R.; Van Gool, L.; Zhang, L.; Yang, M.-H.; Patel, V.M.; Zhang, H.; Sindagi, V.A.; Zhao, R.; Ma, X.; Qin, Y.; Jia, L.; Friedel, K.; Ki, S.; Sim, H.; Choi, J.-S.; Kim, S.; Seo, S.; Kim, M.; Mondal, R.; Santra, S.; Chanda, B.; Liu, J.; Mei, K.; Li, J.; Luyao; Fang, F.; Jiang, A.; Qu, X.; Liu, T.; Wang, P.; Sun, B.; Deng, J.; Zhao, Y.; Hong, M.; Huang, J.; Chen, Y.; Chen, E.; Yu, X.; Wu, T.; Genc, A.; Engin, D.; Ekenel, H.K.; Liu, W.; Tong, T.; Li, G.; Gao, Q.; Li, Z.; Tang, D.; Huo, Z.; Alvarez-Gila, A.; Galdran, A.; Bria, A.; Vazquez-Corral, J.; Bertalmo, M.; Demir, H.S.; Adil, O.F.; Phung, H.X.; Jin, X.; Chen, J.; Shan, C.; Chen, Z.
Source
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) CVPRW Computer Vision and Pattern Recognition Workshops (CVPRW), 2018 IEEE/CVF Conference on. :1004-100410 Jun, 2018
Subject
Language
ISSN
2160-7516
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.