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

May 04, 2026 · Grace Period · + Add venue

Authors Jiebin Yan, Chenyu Tu, Weixia Zhang, Zhihua Wang, Peibei Cao, Qinghua Lin, Yuming Fang, Xiaoning Liu, Zongwei Wu, Zhuyun Zhou, Radu Timofte arXiv ID 2605.02212 Category cs.CV: Computer Vision Citations 0

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.