NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917457346691072 |
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| author | Yan, Jiebin Tu, Chenyu Zhang, Weixia Wang, Zhihua Cao, Peibei Lin, Qinghua Fang, Yuming Liu, Xiaoning Wu, Zongwei Zhou, Zhuyun Timofte, Radu |
| author_facet | Yan, Jiebin Tu, Chenyu Zhang, Weixia Wang, Zhihua Cao, Peibei Lin, Qinghua Fang, Yuming Liu, Xiaoning Wu, Zongwei Zhou, Zhuyun Timofte, Radu |
| contents | This paper presents a comprehensive review of the NITRE 2026 Efficient Low Light Image Enhancement (E-LLIE) Challenge, highlighting the proposed solutions and final outcomes. This challenge focuses on mobile image enhancement under low-light conditions, aiming to design lightweight networks that improve enhancement quality while ensuring practical deployability under limited computational resources. A total of 207 participants registered, 27 teams submitted valid entries, and 17 teams ultimately provided valid factsheet. Based on these submissions, this paper provides a systematic evaluation of recent methods for E-LLIE, offering a comprehensive overview of state-of-the-art progress and demonstrating significant improvements in both performance and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_02212 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results Yan, Jiebin Tu, Chenyu Zhang, Weixia Wang, Zhihua Cao, Peibei Lin, Qinghua Fang, Yuming Liu, Xiaoning Wu, Zongwei Zhou, Zhuyun Timofte, Radu Computer Vision and Pattern Recognition This paper presents a comprehensive review of the NITRE 2026 Efficient Low Light Image Enhancement (E-LLIE) Challenge, highlighting the proposed solutions and final outcomes. This challenge focuses on mobile image enhancement under low-light conditions, aiming to design lightweight networks that improve enhancement quality while ensuring practical deployability under limited computational resources. A total of 207 participants registered, 27 teams submitted valid entries, and 17 teams ultimately provided valid factsheet. Based on these submissions, this paper provides a systematic evaluation of recent methods for E-LLIE, offering a comprehensive overview of state-of-the-art progress and demonstrating significant improvements in both performance and efficiency. |
| title | NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.02212 |