The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
This challenge provides a standardized benchmark for evaluating unified image restoration methods across multiple real-world degradations, benefiting researchers in low-level vision.
The paper reviews the second LoViF Challenge on real-world all-in-one image restoration, which attracted 158 participants and 20 final teams. It provides a benchmark and analysis of methods for restoring images degraded by blur, low-light, haze, rain, and snow.
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.