NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results

Jiebin Yan; Chenyu Tu; Weixia Zhang; Zhihua Wang; Peibei Cao; Qinghua Lin; Yuming Fang; Xiaoning Liu; Zongwei Wu; Zhuyun Zhou; Radu Timofte

NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results

Computer Vision and Pattern Recognition 2026-05-05 v1

Authors: Jiebin Yan , Chenyu Tu , Weixia Zhang , Zhihua Wang , Peibei Cao , Qinghua Lin , Yuming Fang , Xiaoning Liu , Zongwei Wu , Zhuyun Zhou , Radu Timofte

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Abstract

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.

Keywords

low light image enhancement efficient networks NTIRE challenge mobile deployment image quality

Cite

@article{arxiv.2605.02212,
  title  = {NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results},
  author = {Jiebin Yan and Chenyu Tu and Weixia Zhang and Zhihua Wang and Peibei Cao and Qinghua Lin and Yuming Fang and Xiaoning Liu and Zongwei Wu and Zhuyun Zhou and Radu Timofte},
  journal= {arXiv preprint arXiv:2605.02212},
  year   = {2026}
}

NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results

Abstract

Keywords

Cite

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