LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
For researchers in low-level vision, this challenge establishes a standardized benchmark to evaluate and compare all-in-one restoration methods under diverse real-world degradations.
The LoViF 2026 Challenge provided a unified benchmark for real-world all-in-one image restoration across blur, low-light, haze, rain, and snow, attracting 124 participants and 9 valid submissions, advancing the state of the art in unified restoration.
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.