NTIRE 2023 Challenge on Image Denoising: Methods and Results

Yawei Li; Yulun Zhang; Luc Van Gool; Radu Timofte; 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-Corral; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong

doi:10.1109/CVPRW59228.2023.00188

NTIRE 2023 Challenge on Image Denoising: Methods and Results

Yawei Li^*, Yulun Zhang^*, Luc Van Gool^*, Radu Timofte^*, 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 WangYuqian 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-Corral, Konstantinos G. Derpanis, Michael S. Brown, Hao Li, Zhihao Zhao, Jinshan Pan, Jiangxin Dong

^*Corresponding author for this work

Research output: Other contribution

13 Citations (Scopus)

Abstract

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.

Original language	English
Number of pages	17
ISBN (Electronic)	9798350302493
DOIs	https://doi.org/10.1109/CVPRW59228.2023.00188
Publication status	Published - 2023

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2023-June
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

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10.1109/CVPRW59228.2023.00188

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title = "NTIRE 2023 Challenge on Image Denoising: Methods and Results",

abstract = "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.",

author = "Yawei Li and Yulun Zhang and {Van Gool}, Luc and Radu Timofte 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 Zamir, {Syed Waqas} and Javier Vazquez-Corral and Derpanis, {Konstantinos G.} and Brown, {Michael S.} and Hao Li and Zhihao Zhao and Jinshan Pan and Jiangxin Dong",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.",

year = "2023",

doi = "10.1109/CVPRW59228.2023.00188",

language = "English",

isbn = "979-8-3503-0250-9",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

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Li, Y, Zhang, Y, Van Gool, L, Timofte, R, Tu, Z, Du, K, Wang, H, Chen, H, Li, W, Wang, X, Hu, J, Wang, Y, Kong, X, Wu, J, Zhang, D, Zhang, J, Liu, S, Bai, F, Feng, C, Wang, H, Zhang, Y, Shao, G, Wang, X, Lei, L, Xu, R, Zhang, Z, Chen, Y, Ren, D, Zuo, W, Wu, Q, Han, M, Cheng, S, Li, H, Jiang, T, Jiang, C, Li, X, Luo, J, Lin, W, Yu, L, Fan, H, Liu, S, Arora, A, Zamir, SW, Vazquez-Corral, J, Derpanis, KG, Brown, MS, Li, H, Zhao, Z, Pan, J & Dong, J 2023, NTIRE 2023 Challenge on Image Denoising: Methods and Results.. https://doi.org/10.1109/CVPRW59228.2023.00188

TY - GEN

T1 - NTIRE 2023 Challenge on Image Denoising

T2 - Methods and Results

AU - Li, Yawei

AU - Zhang, Yulun

AU - Van Gool, Luc

AU - Timofte, Radu

AU - Tu, Zhijun

AU - Du, Kunpeng

AU - Wang, Hailing

AU - Chen, Hanting

AU - Li, Wei

AU - Wang, Xiaofei

AU - Hu, Jie

AU - Wang, Yunhe

AU - Kong, Xiangyu

AU - Wu, Jinlong

AU - Zhang, Dafeng

AU - Zhang, Jianxing

AU - Liu, Shuai

AU - Bai, Furui

AU - Feng, Chaoyu

AU - Wang, Hao

AU - Zhang, Yuqian

AU - Shao, Guangqi

AU - Wang, Xiaotao

AU - Lei, Lei

AU - Xu, Rongjian

AU - Zhang, Zhilu

AU - Chen, Yunjin

AU - Ren, Dongwei

AU - Zuo, Wangmeng

AU - Wu, Qi

AU - Han, Mingyan

AU - Cheng, Shen

AU - Li, Haipeng

AU - Jiang, Ting

AU - Jiang, Chengzhi

AU - Li, Xinpeng

AU - Luo, Jinting

AU - Lin, Wenjie

AU - Yu, Lei

AU - Fan, Haoqiang

AU - Liu, Shuaicheng

AU - Arora, Aditya

AU - Zamir, Syed Waqas

AU - Vazquez-Corral, Javier

AU - Derpanis, Konstantinos G.

AU - Brown, Michael S.

AU - Li, Hao

AU - Zhao, Zhihao

AU - Pan, Jinshan

AU - Dong, Jiangxin

PY - 2023

Y1 - 2023

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85170825877&partnerID=8YFLogxK

U2 - 10.1109/CVPRW59228.2023.00188

DO - 10.1109/CVPRW59228.2023.00188

M3 - Other contribution

AN - SCOPUS:85170825877

SN - 979-8-3503-0250-9

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

ER -

NTIRE 2023 Challenge on Image Denoising: Methods and Results

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