NTIRE 2025 Challenge on Single Image Reflection Removal in the Wild: Datasets, Methods and Results

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Kangning Yang, Jie Cai, Ling Ouyang, Florin-Alexandru Vasluianu, Radu Timofte, Jiaming Ding, Huiming Sun, Lan Fu, Jinlong Li, Chiu Man Ho, Zibo Meng, Mingjia Li, Hainuo Wang, Qiming Hu, Jiarui Wang, Hao Zhao, Jin Hu, Xiaojie Guo, Mengru Yang, Jing He et al. (29 additional authors not shown)

Published: 2025, Last Modified: 14 Nov 2025CVPR Workshops 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: In this paper, we review the NTIRE 2025 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, participants are required to process real-world images that cover a range of reflection scenarios and intensities, with the goal of generating clean images without reflections. The challenge attracted more than 200 registrations, with 11 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 the five experts in the field. The proposed datasets are available at https://huggingface.co/datasets/qiuzhangTiTi/NTIRE2025-SIRR and the homepage of this challenge is at https://github.com/caijie0620/Reflection-Removal-in-the-Wild.