The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results

Xiang Chen, Hao Li, Jiangxin Dong, Jinshan Pan, Xin Li, Hongbo Ding, Junpeng Jiang, Xingyu Qiu, Yilian Zhong, Yuxiang Chen, Shibo Yin, Zixuan Huang, Yushun Fang, Xilei Zhu, Yahui Wang, Chen Lu, Xiaodong Zhou, Qingyue Cao, Changwei Gong, Jingyun Liu, Xingchen Yi, Hansen Shi, Ruiyi Liu, Jirui Xie, Tao Liu, Wenzhuo Ma, Hongzhen Li, Yongyong Chen, Zheng Zhou, Jingyong Su, Jie Liu, Haijin Zeng, Cheng Li, Peishuai Zha, Ziyi Wang, Jian Tang, Yan Chen, Long Bao, Heng Sun, Jiyuan Zhang, Shuai Liu, Wei Ding, Chengjun Guo, Yibin Huang, Xiaotao Wang, Dongqing Zou, Lei Lei, Xiaofeng Wang, Xiao Liu, Yulin Wu, Yuhan Zhao, Shurui Peng, Chao Ren, Yu-Kai Wang, Kosuke Shigematsu, Asuka Shin, Rong-Lin Jian, Cheng-Jun Kang, Jin-Hui Jiang, Jialin Zhou, Kuo Yuan, Songyu Zhang, E B Benson, Ashfaq Hussain, Pruthvikanth AC, Qirui Chen, Jinyuan Chen, Jun Zhang, Xu Zhang, Xuhui Cao, Jiaqi Ma, Laibin Chang, Yuchun Miao, Yichu Xu, Yuanzhi Yao, Shi Chen, Yuning Cui, Huan Zhang, Lefei Zhang, Saeed Ahmad, Ik Hyun Lee, Jun Young Park, Ji Hwan Yoon, Shangquan Sun, Behrooz Nobahar-Moghanlou, Majid Edalatjou, Karim Shahi-Niyar, Ruibo Zhang, Dexiang Hong, Xinyan Liu, Shengeng Tang, Weidong Chen

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.