PT Unknown
AU Yawei Li
   Yulun Zhang
   Radu Timofte
   Luc Van Gool
   Zhijun Tu
   Kunpeng Du
   Hailing Wang
   Hanting Chen
   Wei Li
   Xiaofei Wang
   Jie Hu
   Yunhe Wang
   Xiangyu Kong
   Jinlong Wu
   Dafeng Zhang
   Jianxing Zhang
   Shuai Liu
   Furui Bai
   Chaoyu Feng
   Hao Wang
   Yuqian Zhang
   Guangqi Shao
   Xiaotao Wang
   Lei Lei
   Rongjian Xu
   Zhilu Zhang
   Yunjin Chen
   Dongwei Ren
   Wangmeng Zuo
   Qi Wu
   Mingyan Han
   Shen Cheng
   Haipeng Li
   Ting Jiang
   Chengzhi Jiang
   Xinpeng Li
   Jinting Luo
   Wenjie Lin
   Lei Yu
   Haoqiang Fan
   Shuaicheng Liu
   Aditya Arora
   Syed Waqas Zamir
   Javier Vazquez
   Konstantinos G. Derpanis
   Michael S. Brown
   Hao Li
   Zhihao Zhao
   Jinshan Pan
   Jiangxin Dong
   Jinhui Tang
   Bo Yang
   Jingxiang Chen
   Chenghua Li
   Xi Zhang
   Zhao Zhang
   Jiahuan Ren
   Zhicheng Ji
   Kang Miao
   Suiyi Zhao
   Huan Zheng
   YanYan Wei
   Kangliang Liu
   Xiangcheng Du
   Sijie Liu
   Yingbin Zheng
   Xingjiao Wu
   Cheng Jin
   Rajeev Irny
   Sriharsha Koundinya
   Vighnesh Kamath
   Gaurav Khandelwal
   Sunder Ali Khowaja
   Jiseok Yoon
   Ik Hyun Lee
   Shijie Chen
   Chengqiang Zhao
   Huabin Yang
   Zhongjian Zhang
   Junjia Huang
   Yanru Zhang
TI NTIRE 2023 challenge on image denoising: Methods and results
BT Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
PY 2023
BP 1904
EP 1920
DI 10.1109/CVPRW59228.2023.00188
AB This paper reviews the NTIRE 2023 challenge on image denoising (σ = 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.
ER