학술논문
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Document Type
Conference
Author
Abdelhamed, Abdelrahman; Afifi, Mahmoud; Timofte, Radu; Brown, Michael S.; Cao, Yue; Zhang, Zhilu; Zuo, Wangmeng; Zhang, Xiaoling; Liu, Jiye; Chen, Wendong; Wen, Changyuan; Liu, Meng; Lv, Shuailin; Zhang, Yunchao; Pan, Zhihong; Li, Baopu; Xi, Teng; Fan, Yanwen; Yu, Xiyu; Zhang, Gang; Liu, Jingtuo; Han, Junyu; Ding, Errui; Yu, Songhyun; Park, Bumjun; Jeong, Jechang; Liu, Shuai; Zong, Ziyao; Nan, Nan; Li, Chenghua; Yang, Zengli; Bao, Long; Wang, Shuangquan; Bai, Dongwoon; Lee, Jungwon; Kim, Youngjung; Rho, Kyeongha; Shin, Changyeop; Kim, Sungho; Tang, Pengliang; Zhao, Yiyun; Zhou, Yuqian; Fan, Yuchen; Huang, Thomas; Li, Zhihao; Shah, Nisarg A.; Liu, Wei; Yan, Qiong; Zhao, Yuzhi; Mozejko, Marcin; Latkowski, Tomasz; Treszczotko, Lukasz; Szafraniuk, Michal; Trojanowski, Krzysztof; Wu, Yanhong; Michelini, Pablo Navarrete; Hu, Fengshuo; Lu, Yunhua; Kim, Sujin; Kim, Wonjin; Lee, Jaayeon; Choi, Jang-Hwan; Zhussip, Magauiya; Khassenov, Azamat; Kim, Jong Hyun; Cho, Hwechul; Kansal, Priya; Nathan, Sabari; Ye, Zhangyu; Lu, Xiwen; Wu, Yaqi; Yang, Jiangxin; Cao, Yanlong; Tang, Siliang; Cao, Yanpeng; Maggioni, Matteo; Marras, Ioannis; Tanay, Thomas; Slabaugh, Gregory; Yan, Youliang; Kang, Myungjoo; Choi, Han-Soo; Song, Kyungmin; Xu, Shusong; Lu, Xiaomu; Wang, Tingniao; Lei, Chunxia; Liu, Bin; Gupta, Rajat; Kumar, Vineet
Source
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Computer Vision and Pattern Recognition Workshops (CVPRW),2020 IEEE/CVF Conference on. :2077-2088 Jun, 2020
Subject
Language
ISSN
2160-7516
Abstract
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.