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arxiv: 2005.04117 · v1 · pith:XQT7GIG6new · submitted 2020-05-08 · 💻 cs.CV · eess.IV

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

Abdelrahman Abdelhamed , Mahmoud Afifi , Radu Timofte , Michael S. Brown , Yue Cao , Zhilu Zhang , Wangmeng Zuo , Xiaoling Zhang
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Jiye Liu Wendong Chen Changyuan Wen Meng Liu Shuailin Lv Yunchao Zhang Zhihong Pan Baopu Li Teng Xi Yanwen Fan Xiyu Yu Gang Zhang Jingtuo Liu Junyu Han Errui Ding Songhyun Yu Bumjun Park Jechang Jeong Shuai Liu Ziyao Zong Nan Nan Chenghua Li Zengli Yang Long Bao Shuangquan Wang Dongwoon Bai Jungwon Lee Youngjung Kim Kyeongha Rho Changyeop Shin Sungho Kim Pengliang Tang Yiyun Zhao Yuqian Zhou Yuchen Fan Thomas Huang Zhihao Li Nisarg A. Shah Wei Liu Qiong Yan Yuzhi Zhao Marcin Mo\.zejko Tomasz Latkowski Lukasz Treszczotko Micha{\l} Szafraniuk Krzysztof Trojanowski Yanhong Wu Pablo Navarrete Michelini Fengshuo Hu Yunhua Lu Sujin Kim Wonjin Kim Jaayeon Lee Jang-Hwan Choi Magauiya Zhussip Azamat Khassenov Jong Hyun Kim Hwechul Cho Priya Kansal Sabari Nathan Zhangyu Ye Xiwen Lu Yaqi Wu Jiangxin Yang Yanlong Cao Siliang Tang Yanpeng Cao Matteo Maggioni Ioannis Marras Thomas Tanay Gregory Slabaugh Youliang Yan Myungjoo Kang Han-Soo Choi Kyungmin Song Shusong Xu Xiaomu Lu Tingniao Wang Chunxia Lei Bin Liu Rajat Gupta Vineet Kumar
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classification 💻 cs.CV eess.IV
keywords challengeimagedenoisingmethodsrealnewlyntiresidd
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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.

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