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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2604.10321 |
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| author | Cai, Jie Yang, Kangning Li, Zhiyuan Vasluianu, Florin-Alexandru Timofte, Radu Li, Jinlong Shen, Jinglin Meng, Zibo Cao, Junyan Zhao, Lu Liu, Pengwei Zhang, Yuyi Guo, Fengjun Hu, Jiagao Wang, Zepeng Wang, Fei Zhou, Daiguo Chen, Yi'ang Zhu, Honghui Yang, Mengru Luo, Yan Jiang, Kui Guo, Jin Park, Jonghyuk Sim, Jae-Young Zhou, Wei Huang, Hongyu Li, Linfeng Kong, Lindong Meesiyawar, Saiprasad Khanpagadi, Misbha Falak Akalwadi, Nikhil Tabib, Ramesh Ashok Mudenagudi, Uma Benjdira, Bilel Ali, Anas M. Boulila, Wadii Shigematsu, Kosuke Shirono, Hiroto Shin, Asuka Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Tu, Jiachen Joshi, Shreeniketh Jiang, Jin-Hui Lin, Yu-Fan Hsiao, Yu-Jou Lee, Chia-Ming Yang, Fu-En Wang, Yu-Chiang Frank Hsu, Chih-Chung |
| author_facet | Cai, Jie Yang, Kangning Li, Zhiyuan Vasluianu, Florin-Alexandru Timofte, Radu Li, Jinlong Shen, Jinglin Meng, Zibo Cao, Junyan Zhao, Lu Liu, Pengwei Zhang, Yuyi Guo, Fengjun Hu, Jiagao Wang, Zepeng Wang, Fei Zhou, Daiguo Chen, Yi'ang Zhu, Honghui Yang, Mengru Luo, Yan Jiang, Kui Guo, Jin Park, Jonghyuk Sim, Jae-Young Zhou, Wei Huang, Hongyu Li, Linfeng Kong, Lindong Meesiyawar, Saiprasad Khanpagadi, Misbha Falak Akalwadi, Nikhil Tabib, Ramesh Ashok Mudenagudi, Uma Benjdira, Bilel Ali, Anas M. Boulila, Wadii Shigematsu, Kosuke Shirono, Hiroto Shin, Asuka Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Tu, Jiachen Joshi, Shreeniketh Jiang, Jin-Hui Lin, Yu-Fan Hsiao, Yu-Jou Lee, Chia-Ming Yang, Fu-En Wang, Yu-Chiang Frank Hsu, Chih-Chung |
| contents | In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academic research, most methods are tested on synthetic images or limited real-world images, creating a gap in real-world applications. In this challenge, we provide participants with the OpenRR-5k dataset. This dataset requires participants to process real-world images covering a range of reflection scenarios and intensities, aiming to generate clean images without reflections. The challenge attracted more than 100 registrations, with eleven of them participating in the final testing phase. The top-ranked methods advanced the state-of-the-art reflection removal performance and earned unanimous recognition from five experts in the field. The proposed OpenRR-5k dataset is available at https://huggingface.co/datasets/qiuzhangTiTi/OpenRR-5k, and the homepage of this challenge is at https://github.com/caijie0620/OpenRR-5k. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10321 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods Cai, Jie Yang, Kangning Li, Zhiyuan Vasluianu, Florin-Alexandru Timofte, Radu Li, Jinlong Shen, Jinglin Meng, Zibo Cao, Junyan Zhao, Lu Liu, Pengwei Zhang, Yuyi Guo, Fengjun Hu, Jiagao Wang, Zepeng Wang, Fei Zhou, Daiguo Chen, Yi'ang Zhu, Honghui Yang, Mengru Luo, Yan Jiang, Kui Guo, Jin Park, Jonghyuk Sim, Jae-Young Zhou, Wei Huang, Hongyu Li, Linfeng Kong, Lindong Meesiyawar, Saiprasad Khanpagadi, Misbha Falak Akalwadi, Nikhil Tabib, Ramesh Ashok Mudenagudi, Uma Benjdira, Bilel Ali, Anas M. Boulila, Wadii Shigematsu, Kosuke Shirono, Hiroto Shin, Asuka Xu, Guoyi Jiang, Yaoxin Liu, Jiajia Shi, Yaokun Tu, Jiachen Joshi, Shreeniketh Jiang, Jin-Hui Lin, Yu-Fan Hsiao, Yu-Jou Lee, Chia-Ming Yang, Fu-En Wang, Yu-Chiang Frank Hsu, Chih-Chung Computer Vision and Pattern Recognition In this paper, we review the NTIRE 2026 challenge on single-image reflection removal (SIRR) in the wild. SIRR is a fundamental task in image restoration. Despite progress in academic research, most methods are tested on synthetic images or limited real-world images, creating a gap in real-world applications. In this challenge, we provide participants with the OpenRR-5k dataset. This dataset requires participants to process real-world images covering a range of reflection scenarios and intensities, aiming to generate clean images without reflections. The challenge attracted more than 100 registrations, with eleven of them participating in the final testing phase. The top-ranked methods advanced the state-of-the-art reflection removal performance and earned unanimous recognition from five experts in the field. The proposed OpenRR-5k dataset is available at https://huggingface.co/datasets/qiuzhangTiTi/OpenRR-5k, and the homepage of this challenge is at https://github.com/caijie0620/OpenRR-5k. |
| title | NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.10321 |