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Main Authors: Sun, Lei, Guo, Hang, Ren, Bin, Van Gool, Luc, Timofte, Radu, Li, Yawei, Kong, Xiangyu, Park, Hyunhee, Yu, Xiaoxuan, Han, Suejin, Jeon, Hakjae, Li, Jia, Chun, Hyung-Ju, Ryou, Donghun, Ha, Inju, Han, Bohyung, Ma, Jingyu, Huang, Zhijuan, Fu, Huiyuan, Yu, Hongyuan, Zhang, Boqi, Shi, Jiawei, Zhang, Heng, Ma, Huadong, Tyagi, Deepak Kumar, Kukretti, Aman, Sharma, Gajender, Koundinya, Sriharsha, Manna, Asim, Cheng, Jun, Tan, Shan, Liu, Jun, Hao, Jiangwei, Luo, Jianping, Lu, Jie, Tazi, Satya Narayan, Gautam, Arnim, Pawar, Aditi, Joshi, Aishwarya, Dudhane, Akshay, Hambadre, Praful, Chaudhary, Sachin, Vipparthi, Santosh Kumar, Murala, Subrahmanyam, Tu, Jiachen, Akalwadi, Nikhil, Aralikatti, Vijayalaxmi Ashok, Hegde, Dheeraj Damodar, Rao, G Gyaneshwar, Kalal, Jatin, Desai, Chaitra, Tabib, Ramesh Ashok, Mudenagudi, Uma, Lin, Zhenyuan, Dong, Yubo, Li, Weikun, Li, Anqi, Gao, Ang, Yuan, Weijun, Li, Zhan, Deng, Ruting, Chen, Yihang, Deng, Yifan, Chen, Zhanglu, Yao, Boyang, Zheng, Shuling, Zhang, Feng, Fu, Zhiheng, Ali, Anas M., Benjdira, Bilel, Boulila, Wadii, Seny, Jan, Zhou, Pei, Hu, Jianhua, Law, K. L. Eddie, Lee, Jaeho, Rasool, M. J. Aashik, Rehman, Abdur, Sharif, SMA, Kim, Seongwan, Brateanu, Alexandru, Balmez, Raul, Orhei, Ciprian, Ancuti, Cosmin, Xiao, Zeyu, Li, Zhuoyuan, Wang, Ziqi, Wei, Yanyan, Wang, Fei, Li, Kun, Tang, Shengeng, Zhang, Yunkai, Zhou, Weirun, Lu, Haoxuan
Format: Preprint
Published: 2025
Subjects:
Computer Vision and Pattern Recognition
Online Access:https://arxiv.org/abs/2504.12276
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Table of Contents:
  • This paper presents an overview of the NTIRE 2025 Image Denoising Challenge (σ = 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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