@InProceedings{YaweiLi2023,
author="Yawei Li
and Yulun Zhang
and Radu Timofte
and Luc Van Gool
and Zhijun Tu
and Kunpeng Du
and Hailing Wang
and Hanting Chen
and Wei Li
and Xiaofei Wang
and Jie Hu
and Yunhe Wang
and Xiangyu Kong
and Jinlong Wu
and Dafeng Zhang
and Jianxing Zhang
and Shuai Liu
and Furui Bai
and Chaoyu Feng
and Hao Wang
and Yuqian Zhang
and Guangqi Shao
and Xiaotao Wang
and Lei Lei
and Rongjian Xu
and Zhilu Zhang
and Yunjin Chen
and Dongwei Ren
and Wangmeng Zuo
and Qi Wu
and Mingyan Han
and Shen Cheng
and Haipeng Li
and Ting Jiang
and Chengzhi Jiang
and Xinpeng Li
and Jinting Luo
and Wenjie Lin
and Lei Yu
and Haoqiang Fan
and Shuaicheng Liu
and Aditya Arora
and Syed Waqas Zamir
and Javier Vazquez
and Konstantinos G. Derpanis
and Michael S. Brown
and Hao Li
and Zhihao Zhao
and Jinshan Pan
and Jiangxin Dong
and Jinhui Tang
and Bo Yang
and Jingxiang Chen
and Chenghua Li
and Xi Zhang
and Zhao Zhang
and Jiahuan Ren
and Zhicheng Ji
and Kang Miao
and Suiyi Zhao
and Huan Zheng
and YanYan Wei
and Kangliang Liu
and Xiangcheng Du
and Sijie Liu
and Yingbin Zheng
and Xingjiao Wu
and Cheng Jin
and Rajeev Irny
and Sriharsha Koundinya
and Vighnesh Kamath
and Gaurav Khandelwal
and Sunder Ali Khowaja
and Jiseok Yoon
and Ik Hyun Lee
and Shijie Chen
and Chengqiang Zhao
and Huabin Yang
and Zhongjian Zhang
and Junjia Huang
and Yanru Zhang",
title="NTIRE 2023 challenge on image denoising: Methods and results",
booktitle="Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops",
year="2023",
pages="1904--1920",
abstract="This paper reviews the NTIRE 2023 challenge on image denoising ($\sigma$ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising.",
optnote="MACO; CIC",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3910), last updated on Fri, 19 Jan 2024 12:45:09 +0100",
doi="10.1109/CVPRW59228.2023.00188",
opturl="https://ieeexplore.ieee.org/document/10208539"
}