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Low Light Image Enhancement Challenge at NTIRE 2026

2026-04-19 23:44:34
George Ciubotariu, Sharif S M A, Abdur Rehman, Fayaz Ali Dharejo, Rizwan Ali Naqvi, Marcos V. Conde, Radu Timofte, Zhi Jin, Hongjun Wu, Wenjian Zhang, Chang Ye, Xunpeng Yi, Qinglong Yan, Yibing Zhang, Nikhil Akalwadi, Varda I Pattanshetty, Varsha I Pattanshetty, Padmashree Desai, Uma Mudenagudi, Ramesh Ashok Tabib, Hao Yang, Ruikun Zhang, Liyuan Pan, Furkan K{\i}nl{\i}, Donghun Ryou, Inju Ha, Junoh Kang, Bohyung Han, Wei Zhou, Yuval Haitman, Ariel Lapid, Reuven Peretz, Idit Diamant, Leilei Cao, Shuo Zhang, Praful Hambarde, Prateek Shaily, Jayant Kumar, Hardik Sharma, Aashish Negi, Sachin Chaudhary, Akshay Dudhane, Amit Shukla, MoHao Wu, Lin Wang, Jiachen Tu, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Yaokun Shi, Raul Balmez, Alexandru Brateanu, Ciprian Orhei, Cosmin Ancuti, Codruta O. Ancuti, Bilel Benjdira, Anas M. Ali, Wadii Boulila, Kaifan Qiao, Bofei Chen, Jingyi Xu, Duo Zhang, Xin Deng, Mai Xu, Shengxi Li, Lai Jiang, Harini A, Ananya N, Lakshanya K, Ying Xu, Xinyi Zhu, Shijun Shi, Jiangning Zhang, Yong Liu, Kai Hu, Jing Xu, Xianfang Zeng, Jinao Song, Guangsheng Tang, Cheng Li, Yuqiang Yang, Ziyi Wang, Yan Chen, Long Bao, Heng Sun, Mohab Kishawy, Jun Chen, Wan-Chi Siu, Yihao Cheng, Hon Man Hammond Lee, Chun-Chuen Hui
arXiv_CV
           
Abstract

This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions by learning representative visual cues with the purpose of restoring information loss due to low-contrast and noisy images. A total of 195 participants registered for the first track and 153 for the second track of the competition, and 22 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in (joint denoising and) low-light image enhancement, showcasing the significant progress in the field, while leveraging samples of our novel dataset.

Abstract (translated)

URL

https://arxiv.org/abs/2604.17669

PDF

https://arxiv.org/pdf/2604.17669.pdf

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