20支团队攻克真实世界图像修复难题，验证统一框架有效性
The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
- 构建统一框架评估多种退化条件下的图像修复能力
- 20支队伍经验证后入围，实现多类退化场景的联合恢复
- 为真实低级视觉研究提供新基准，适合图像修复方向研究者
本文回顾了第二届LoViF 2026挑战赛在真实世界全类型图像修复领域的成果。挑战赛聚焦于多种真实退化条件（如模糊、低光、雾霾、雨雪）下的统一图像修复，提供统一评估框架以衡量模型的修复精度、鲁棒性与泛化能力。共有158支队伍注册，经结果复现与验证后，20支团队进入最终排名。本报告全面分析了参赛方案及结果，揭示了当前真实世界全类型图像修复的技术进展，总结出有效的设计策略，并建立了新的基准，为未来低级视觉研究提供参考。
原文摘要 · Abstract (English)
This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provides a common benchmark for evaluating the restoration accuracy, robustness, and generalization capability of models across multiple degradation categories within a unified framework. The competition attracted 158 registered participants, and 20 teams were included in the final ranking after their submitted results were successfully reproduced and verified. This report provides a comprehensive analysis of the submitted solutions and corresponding results, highlighting recent advances in real-world all-in-one image restoration. The summarized methods and empirical findings reveal effective design strategies and establish an updated benchmark for future research in real-world low-level vision.
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