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

2026-05-04

AI Summary

This work addresses the trade-off between performance and efficiency in deploying low-light image enhancement models on mobile devices by establishing the first comprehensive benchmark specifically designed for mobile-oriented, efficient low-light enhancement. The benchmark systematically integrates and evaluates a variety of lightweight neural network architectures alongside deployment optimization strategies. The initiative attracted 207 participants, yielding 27 valid submissions from competing teams, 17 of which provided complete documentation. Experimental results demonstrate significant advances in jointly improving both enhancement quality and computational efficiency under stringent resource constraints, offering practical, reproducible, and deployable solutions for real-world mobile applications.

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.

Problem

• Low Light Image Enhancement
• Efficient Model
• Mobile Deployment
• Computational Constraints
• Lightweight Network

Innovation

• Efficient Low Light Image Enhancement
• Lightweight Network
• Mobile Deployment
• Computational Efficiency
• NTIRE Challenge