OpenRR-5k

The OpenRR-5k dataset is a large-scale benchmark for single-image reflection removal (SIRR) in the wild, introduced as part of the NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods.

The dataset consists of real-world images covering a variety of reflection scenarios and intensities.

GitHub Repository: caijie0620/OpenRR-5k

Dataset Structure

The dataset consists of the following components:

• train_5000.zip: contains 5,000 paired input images and corresponding ground truth (GT) images.
• val_300_blended.zip: contains 300 validation input images.
• val_300_transmission.zip: contains 300 validation ground truth images.
• test_100_blended.zip: contains 100 test input images (without ground truth).

For more details regarding the challenge, please visit the CodaBench Competition page.

Citation

If you find this dataset helpful in your research, please cite the following work:

@inproceedings{cai2025openrr,
  title={Openrr-5k: A large-scale benchmark for reflection removal in the wild},
  author={Cai, Jie and Yang, Kangning and Ouyang, Ling and Fu, Lan and Ding, Jiaming and Shen, Jinglin and Meng, Zibo},
  booktitle={2025 IEEE 8th International Conference on Multimedia Information Processing and Retrieval (MIPR)},
  pages={14--19},
  year={2025},
  organization={IEEE}
}