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The Tenth NTIRE 2025 Image Denoising Challenge Report

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arxiv 2504.12276 v1 pith:MIGXFEJP submitted 2025-04-16 cs.CV

The Tenth NTIRE 2025 Image Denoising Challenge Report

Lei Sun , Hang Guo , Bin Ren , Luc Van Gool , Radu Timofte , Yawei Li , Xiangyu Kong , Hyunhee Park
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Xiaoxuan Yu Suejin Han Hakjae Jeon Jia Li Hyung-Ju Chun Donghun Ryou Inju Ha Bohyung Han Jingyu Ma Zhijuan Huang Huiyuan Fu Hongyuan Yu Boqi Zhang Jiawei Shi Heng Zhang Huadong Ma Deepak Kumar Tyagi Aman Kukretti Gajender Sharma Sriharsha Koundinya Asim Manna Jun Cheng Shan Tan Jun Liu Jiangwei Hao Jianping Luo Jie Lu Satya Narayan Tazi Arnim Gautam Aditi Pawar Aishwarya Joshi Akshay Dudhane Praful Hambadre Sachin Chaudhary Santosh Kumar Vipparthi Subrahmanyam Murala Jiachen Tu Nikhil Akalwadi Vijayalaxmi Ashok Aralikatti Dheeraj Damodar Hegde G Gyaneshwar Rao Jatin Kalal Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Zhenyuan Lin Yubo Dong Weikun Li Anqi Li Ang Gao Weijun Yuan Zhan Li Ruting Deng Yihang Chen Yifan Deng Zhanglu Chen Boyang Yao Shuling Zheng Feng Zhang Zhiheng Fu Anas M. Ali Bilel Benjdira Wadii Boulila Jan Seny Pei Zhou Jianhua Hu K. L. Eddie Law Jaeho Lee M. J. Aashik Rasool Abdur Rehman SMA Sharif Seongwan Kim Alexandru Brateanu Raul Balmez Ciprian Orhei Cosmin Ancuti Zeyu Xiao Zhuoyuan Li Ziqi Wang Yanyan Wei Fei Wang Kun Li Shengeng Tang Yunkai Zhang Weirun Zhou Haoxuan Lu
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classification cs.CV
keywords denoisingchallengeimagenoisentireresultsachievingadditive
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This paper presents an overview of the NTIRE 2025 Image Denoising Challenge ({\sigma} = 50), highlighting the proposed methodologies and corresponding results. The primary objective is to develop a network architecture capable of achieving high-quality denoising performance, quantitatively evaluated using PSNR, without constraints on computational complexity or model size. The task assumes independent additive white Gaussian noise (AWGN) with a fixed noise level of 50. A total of 290 participants registered for the challenge, with 20 teams successfully submitting valid results, providing insights into the current state-of-the-art in image denoising.

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