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- Chun, Se Young
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NTIRE 2019 Image Dehazing Challenge Report
- Author(s)
- Ancuti, Codruta O., Ancuti, Cosmin, Timofte, Radu, Gool, Luc Van, Zhang, Lei, Yang, Ming-Hsuan, Guo, Tiantong, Li, Xuelu, Cherukuri, Venkateswararao, Monga, Vishal, Jiang, Hao, Yang, Siyuan, Liu, Yan, Qu, Xiaochao, Wan, Pengfei, Park, Dongwon, Chun, Se Young, Hong, Ming, Huang, Jinying, Chen, Yizi, Chen, Shuxin, Wang, Bomin, Michelini, Pablo Navarrete, Liu, Hanwen, Zhu, Dan, Liu, Jing, Santra, Sanchayan, Mondal, Ranjan, Chanda, Bhabatosh, Morales, Peter, Klinghoffer, Tzofi, Quan, Le Manh, Kim, Yong-Guk, Liang, Xiao, Li, Runde, Pan, Jinshan, Tang, Jinhui, Purohit, Kuldeep, Suin, Maitreya, Rajagopalan, AN, Schettini, Raimondo, Bianco, Simone, Piccoli, Flavio, Cusano, C, Celona, Luigi, Hwang, Sunhee, Ma, Yu Seung, Byun, Hyeran, Murala, Subrahmanyam, Dudhane, Akshay, Aulakh, Harsh, Zheng, Tianxiang, Zhang, Tao, Qin, Weining, Zhou, Runnan, Wang, Shanhu, Tarel, Jean-Philippe, Wang, Chuansheng, Wu, Jiawei
- Issued Date
- 2019-06-17
- Citation
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- 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.
- Publisher
- IEEE/CVF
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