NTIRE 2026低光增强挑战赛总结，展示前沿算法与数据集进展。
Low Light Image Enhancement Challenge at NTIRE 2026
- 基于真实场景构建新数据集，提升模型对复杂低光条件的适应性。
- 195支队伍参与第一赛道，22支团队提交有效成果，性能显著提升。
- 聚焦去噪与增强联合优化，推动低光图像恢复技术进步。
本文全面回顾了NTIRE 2026低光图像增强挑战赛，重点介绍参赛方案与最终结果。该挑战旨在识别能够应对多样复杂条件、生成更清晰且视觉吸引人的图像的有效网络，通过学习代表性视觉线索来恢复因低对比度和噪声导致的信息损失。第一赛道共有195名参与者注册，第二赛道有153人，最终22支团队提交了有效作品。论文系统评估了当前（联合去噪与）低光图像增强领域的最新进展，展示了该方向的显著突破，并利用我们新构建的数据集中的样本进行验证。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。