AI 中文总结
第二届LoViF 2026真实世界一体化图像恢复挑战赛为评估模型在多种退化条件下的性能提供基准，吸引众多参与者。报告全面分析提交方案与结果，揭示有效策略，为真实世界低级别视觉研究建立新基准。
AI 中文摘要
本文对第二届真实世界一体化图像恢复的LoViF挑战赛进行了综述。该挑战赛旨在推动在包括模糊、低光照、雾霾、雨、雪等多种真实世界退化条件下的统一图像恢复。它为评估模型在统一框架内跨多个退化类别的恢复准确性、鲁棒性和泛化能力提供了通用基准。竞赛吸引了158名注册参与者，20支队伍的提交结果经成功复现和验证后进入最终排名。本报告对提交的解决方案和相应结果进行了全面分析，突出了真实世界一体化图像恢复的最新进展。总结的方法和实证结果揭示了有效的设计策略，并为未来真实世界低级别视觉研究建立了更新的基准。
英文摘要
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.
CommentsECCV 2026 Workshops; https://lowlevelcv.com/
