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

8 May 2020
A. Abdelhamed
Mahmoud Afifi
Radu Timofte
M. S. Brown
Yue Cao
Zhilu Zhang
W. Zuo
Xiaolin Zhang
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
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
Michal Szafraniuk
K. Trojanowski
Yanhong Wu
Pablo Navarrete Michelini
Fengshuo Hu
Yunhua Lu
Sujin Kim
Wonjin Kim
Jaayeon Lee
Jang-Hwan Choi
Magauiya Zhussip
A. Khassenov
Jong Hyun Kim
Hwechul Cho
Priya Kansal
S. Nathan
Zhangyu Ye
Xiwen Lu
Yaqi Wu
Jiangxin Yang
Yanlong Cao
Siliang Tang
Yanpeng Cao
Matteo Maggioni
Ioannis Marras
T. Tanay
Greg Slabaugh
Youliang Yan
Myung-joo Kang
Han-Soo Choi
Kyungmin Song
Shusong Xu
Xiaomu Lu
Tingniao Wang
C. Lei
Bin Liu
Rajat Gupta
Vineet Kumar
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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.

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