NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results
Xiaoning Liu, Zongwei Wu, Florin-Alexandru Vasluianu, Hailong Yan, Bin Ren, Yulun Zhang, Shuhang Gu, Le Zhang, Ce Zhu, Radu Timofte, Kangbiao Shi, Yixu Feng, Tao Hu, Yu Cao, Peng Wu, Yijin Liang, Yanning Zhang, Qingsen Yan, Han Zhou, Wei Dong, Yan Min, Mohab Kishawy, Jun Chen, Pengpeng Yu, Anjin Park, Seung-Soo Lee, Young-Joon Park, Zixiao Hu, Junyv Liu, Huilin Zhang, Jun Zhang, Fei Wan, Bingxin Xu, Hongzhe Liu, Cheng Xu, Weiguo Pan, Songyin Dai, Xunpeng Yi, Qinglong Yan, Yibing Zhang, Jiayi Ma, Changhui Hu, Kerui Hu, Donghang Jing, Tiesheng Chen, Zhi Jin, Hongjun Wu, Biao Huang, Haitao Ling, Jiahao Wu, Dandan Zhan, G Gyaneshwar Rao, Vijayalaxmi Ashok Aralikatti, Nikhil Akalwadi, Ramesh Ashok Tabib, Uma Mudenagudi, Ruirui Lin, Guoxi Huang, Nantheera Anantrasirichai, Qirui Yang, Alexandru Brateanu, Ciprian Orhei, Cosmin Ancuti, Daniel Feijoo, Juan C. Benito, Álvaro García, Marcos V. Conde, Yang Qin, Raul Balmez, Anas M. Ali, Bilel Benjdira, Wadii Boulila, Tianyi Mao, Huan Zheng, Yanyan Wei, Shengeng Tang, Dan Guo, Zhao Zhang, Sabari Nathan, K Uma, A Sasithradevi, B Sathya Bama, S. Mohamed Mansoor Roomi, Ao Li, Xiangtao Zhang, Zhe Liu, Yijie Tang, Jialong Tang, Zhicheng Fu, Gong Chen, Joe Nasti, John Nicholson, Zeyu Xiao, Zhuoyuan Li, Ashutosh Kulkarni, Prashant W. Patil, Santosh Kumar Vipparthi, Subrahmanyam Murala, Duan Liu, Weile Li, Hangyuan Lu, Rixian Liu, Tengfeng Wang, Jinxing Liang, Chenxin Yu
TL;DR
The NTIRE 2025 Low-Light Image Enhancement Challenge advances LLIE research by evaluating a broad spectrum of approaches under a unified benchmark, highlighting the shift toward Transformer- and diffusion-inspired architectures. The methods leverage multi-scale fusion, Retinex-based priors, and ensemble strategies (self-ensembling and cross-model fusion) to address brightness, noise, and color consistency in challenging lighting. Key contributions include FusionNet-style multi-model fusion, scale-aware Transformer designs with SAM modules, diffusion-guided refinements, and efficient mobile-friendly solutions, all demonstrated on high-resolution and 4K data. The results indicate meaningful progress over NTIRE 2024, with robust performance even under non-uniform illumination and extreme darkness, paving the way for practical LLIE deployment in photography and video applications.
Abstract
This paper presents a comprehensive review of the NTIRE 2025 Low-Light Image Enhancement (LLIE) Challenge, highlighting the proposed solutions and final outcomes. The objective of the challenge is to identify effective networks capable of producing brighter, clearer, and visually compelling images under diverse and challenging conditions. A remarkable total of 762 participants registered for the competition, with 28 teams ultimately submitting valid entries. This paper thoroughly evaluates the state-of-the-art advancements in LLIE, showcasing the significant progress.