评测124支队伍在多种真实退化下的图像修复能力，推动统一修复技术发展。
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
- 搭建统一基准，评估模型在模糊、低光、雾霾等多退化场景下的表现
- 9支有效提交方案在复杂真实退化下展现较强泛化能力
- 为真实世界低层视觉任务提供可复现的基准与方法参考
本文回顾了LoViF 2026年真实世界全功能图像修复挑战赛。该挑战旨在推动在多种真实退化条件下（包括模糊、低光、雾霾、雨雪）的全功能图像修复研究，提供统一基准以评估修复模型在多个退化类别中的鲁棒性与泛化能力。比赛吸引124名注册参与者，共收到9份有效最终提交及配套说明文档，显著促进真实世界全功能图像修复的发展。本报告详细分析了提交的方法与结果，重点展示近期统一图像修复技术的进展，提出有效策略并建立未来研究的基准。
原文摘要 · Abstract (English)
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provided a unified benchmark to evaluate the robustness and generalization ability of restoration models across multiple degradation categories within a common framework. The competition attracted 124 registered participants and received 9 valid final submissions with corresponding fact sheets, significantly contributing to the progress of real-world all-in-one image restoration. This report provides a detailed analysis of the submitted methods and corresponding results, emphasizing recent progress in unified real-world image restoration. The analysis highlights effective approaches and establishes a benchmark for future research in real-world low-level vision.
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