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.
@article{arxiv.2604.19445,
title = {LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results},
author = {Xiang Chen and Hao Li and Jiangxin Dong and Jinshan Pan and Xin Li and Xin He and Naiwei Chen and Shengyuan Li and Fengning Liu and Haoyi Lv and Haowei Peng and Yilian Zhong and Yuxiang Chen and Shibo Yin and Yushun Fang and Xilei Zhu and Yahui Wang and Chen Lu and Kaibin Chen and Xu Zhang and Xuhui Cao and Jiaqi Ma and Ziqi Wang and Shengkai Hu and Yuning Cui and Huan Zhang and Shi Chen and Bin Ren and Lefei Zhang and Guanglu Dong and Qiyao Zhao and Tianheng Zheng and Chunlei Li and Lichao Mou and Chao Ren and Wangzhi Xing and Xin Lu and Enxuan Gu and Jingxi Zhang and Diqi Chen and Qiaosi Yi and Bingcai Wei and Mingyu Liu and Pengyu Wang and Ce Liu and Miaoxin Guan and Boyu Chen and Hongyu Li and Jian Zhu and Xinrui Luo and Ziyang He and Jiayu Wang and Yichen Xiang and Huayi Qi and Haoyu Bian and Yiran Li and Sunlichen Zhou},
journal= {arXiv preprint arXiv:2604.19445},
year = {2026}
}