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arxiv: 2404.14248 · v1 · pith:24CRCXZXnew · submitted 2024-04-22 · 💻 cs.CV

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

Xiaoning Liu , Zongwei Wu , Ao Li , Florin-Alexandru Vasluianu , Yulun Zhang , Shuhang Gu , Le Zhang , Ce Zhu
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Radu Timofte Zhi Jin Hongjun Wu Chenxi Wang Haitao Ling Yuanhao Cai Hao Bian Yuxin Zheng Jing Lin Alan Yuille Ben Shao Jin Guo Tianli Liu Mohao Wu Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Yanning Zhang Qingsen Yan Wenbin Zou Weipeng Yang Yunxiang Li Qiaomu Wei Tian Ye Sixiang Chen Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Zhichao Zuo Mingshen Wang Junhu Wang Yanyan Wei Xiaopeng Sun Yu Gao Jiancheng Huang Hongming Chen Xiang Chen Hui Tang Yuanbin Chen Yuanbo Zhou Xinwei Dai Xintao Qiu Wei Deng Qinquan Gao Tong Tong Mingjia Li Jin Hu Xinyu He Xiaojie Guo Sabarinathan K Uma A Sasithradevi B Sathya Bama S. Mohamed Mansoor Roomi V.Srivatsav Jinjuan Wang Long Sun Qiuying Chen Jiahong Shao Yizhi Zhang Marcos V. Conde Daniel Feijoo Juan C. Benito Alvaro Garc\'ia Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Ali Murtaza Uswah Khairuddin Ahmad 'Athif Mohd Faudzi Sampada Malagi Amogh Joshi Nikhil Akalwadi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Wenyi Lian Wenjing Lian Jagadeesh Kalyanshetti Vijayalaxmi Ashok Aralikatti Palani Yashaswini Nitish Upasi Dikshit Hegde Ujwala Patil Sujata C Xingzhuo Yan Wei Hao Minghan Fu Pooja choksy Anjali Sarvaiya Kishor Upla Kiran Raja Hailong Yan Yunkai Zhang Baiang Li Jingyi Zhang Huan Zheng
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classification 💻 cs.CV
keywords challengeresultsenhancementimagelightntireadvancementsappealing
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This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.

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  1. Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices

    cs.CV 2025-07 unverdicted novelty 5.0

    LiteIE proposes a two-layer backbone-agnostic feature extractor and parameter-free Iterative Restoration Module for unsupervised low-light enhancement, claiming 19.04 dB PSNR on LOL with 0.07% of SOTA parameters and 3...