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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.19445 |
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| _version_ | 1866910153684549632 |
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| author | Chen, Xiang Li, Hao Dong, Jiangxin Pan, Jinshan Li, Xin He, Xin Chen, Naiwei Li, Shengyuan Liu, Fengning Lv, Haoyi Peng, Haowei Zhong, Yilian Chen, Yuxiang Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Chen, Kaibin Zhang, Xu Cao, Xuhui Ma, Jiaqi Wang, Ziqi Hu, Shengkai Cui, Yuning Zhang, Huan Chen, Shi Ren, Bin Zhang, Lefei Dong, Guanglu Zhao, Qiyao Zheng, Tianheng Li, Chunlei Mou, Lichao Ren, Chao Xing, Wangzhi Lu, Xin Gu, Enxuan Zhang, Jingxi Chen, Diqi Yi, Qiaosi Wei, Bingcai Liu, Mingyu Wang, Pengyu Liu, Ce Guan, Miaoxin Chen, Boyu Li, Hongyu Zhu, Jian Luo, Xinrui He, Ziyang Wang, Jiayu Xiang, Yichen Qi, Huayi Bian, Haoyu Li, Yiran Zhou, Sunlichen |
| author_facet | Chen, Xiang Li, Hao Dong, Jiangxin Pan, Jinshan Li, Xin He, Xin Chen, Naiwei Li, Shengyuan Liu, Fengning Lv, Haoyi Peng, Haowei Zhong, Yilian Chen, Yuxiang Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Chen, Kaibin Zhang, Xu Cao, Xuhui Ma, Jiaqi Wang, Ziqi Hu, Shengkai Cui, Yuning Zhang, Huan Chen, Shi Ren, Bin Zhang, Lefei Dong, Guanglu Zhao, Qiyao Zheng, Tianheng Li, Chunlei Mou, Lichao Ren, Chao Xing, Wangzhi Lu, Xin Gu, Enxuan Zhang, Jingxi Chen, Diqi Yi, Qiaosi Wei, Bingcai Liu, Mingyu Wang, Pengyu Liu, Ce Guan, Miaoxin Chen, Boyu Li, Hongyu Zhu, Jian Luo, Xinrui He, Ziyang Wang, Jiayu Xiang, Yichen Qi, Huayi Bian, Haoyu Li, Yiran Zhou, Sunlichen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19445 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results Chen, Xiang Li, Hao Dong, Jiangxin Pan, Jinshan Li, Xin He, Xin Chen, Naiwei Li, Shengyuan Liu, Fengning Lv, Haoyi Peng, Haowei Zhong, Yilian Chen, Yuxiang Yin, Shibo Fang, Yushun Zhu, Xilei Wang, Yahui Lu, Chen Chen, Kaibin Zhang, Xu Cao, Xuhui Ma, Jiaqi Wang, Ziqi Hu, Shengkai Cui, Yuning Zhang, Huan Chen, Shi Ren, Bin Zhang, Lefei Dong, Guanglu Zhao, Qiyao Zheng, Tianheng Li, Chunlei Mou, Lichao Ren, Chao Xing, Wangzhi Lu, Xin Gu, Enxuan Zhang, Jingxi Chen, Diqi Yi, Qiaosi Wei, Bingcai Liu, Mingyu Wang, Pengyu Liu, Ce Guan, Miaoxin Chen, Boyu Li, Hongyu Zhu, Jian Luo, Xinrui He, Ziyang Wang, Jiayu Xiang, Yichen Qi, Huayi Bian, Haoyu Li, Yiran Zhou, Sunlichen Computer Vision and Pattern Recognition 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. |
| title | LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.19445 |