Low Light Image Enhancement Challenge at NTIRE 2026

2026-04-19

AI Summary

This work addresses the challenge of image quality degradation under low-light, low-contrast, and high-noise conditions by organizing an international challenge that proposes and evaluates advanced methods for joint denoising and low-light enhancement. The study introduces a new low-light image dataset captured in realistic and complex scenarios and systematically benchmarks state-of-the-art deep learning approaches submitted by leading teams. These methods incorporate visual cue learning mechanisms to improve restoration performance. The competition attracted 195 and 153 participating teams in two tracks, respectively, with 22 teams submitting valid solutions, significantly advancing the technical frontiers and practical applicability of low-light image enhancement.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions by learning representative visual cues with the purpose of restoring information loss due to low-contrast and noisy images. A total of 195 participants registered for the first track and 153 for the second track of the competition, and 22 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in (joint denoising and) low-light image enhancement, showcasing the significant progress in the field, while leveraging samples of our novel dataset.

Problem

• Low Light Image Enhancement
• Image Denoising
• Information Restoration
• Low-contrast Images
• Visual Quality

Innovation

• low-light image enhancement
• joint denoising
• NTIRE challenge
• image restoration
• visual cue learning