NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods
Jie Cai, Kangning Yang, Zhiyuan Li, Florin-Alexandru Vasluianu, Radu Timofte, Jinlong Li, Jinglin Shen, Zibo Meng, Junyan Cao, Lu Zhao, Pengwei Liu, Yuyi Zhang, Fengjun Guo, Jiagao Hu, Zepeng Wang, Fei Wang, Daiguo Zhou, Yi'ang Chen, Honghui Zhu, Mengru Yang, Yan Luo, Kui Jiang, Jin Guo, Jonghyuk Park, Jae-Young Sim, Wei Zhou, Hongyu Huang, Linfeng Li, Lindong Kong, Saiprasad Meesiyawar, Misbha Falak Khanpagadi, Nikhil Akalwadi, Ramesh Ashok Tabib, Uma Mudenagudi, Bilel Benjdira, Anas M. Ali, Wadii Boulila, Kosuke Shigematsu, Hiroto Shirono, Asuka Shin, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Yaokun Shi, Jiachen Tu, Shreeniketh Joshi, Jin-Hui Jiang, Yu-Fan Lin, Yu-Jou Hsiao, Chia-Ming Lee, Fu-En Yang, Yu-Chiang Frank Wang, Chih-Chung Hsu
Abstract
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.