Flickr1024 is a large-scale stereo dataset, which consists of 1024 high-quality images pairs and covers diverse senarios. This dataset can be employed for stereo image super-resolution (SR). [details]

Github Project Page

• The Flickr1024 dataset can be downloaded via Baidu Drive or Google Drive

• The Flickr1024 dataset is available for non-commercial use only. Therefore, You agree NOT to reproduce, duplicate, copy, sell, trade, or resell any portion of the images and any portion of derived data.
• All images on the Flickr1024 dataset are obtained from Flickr and they are not the property of our laboratory.
• We reserve the right to terminate your access to the Flickr1024 dataset at any time.

We would like to thank Sascha Becher and Tom Bentz for the approval of using their cross-eye stereo photographs.

• @InProceedings{flickr1024,
  author = {Wang, Yingqian and Wang, Longguang and Yang, Jungang and An, Wei and Guo, Yulan},
  title = {Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
  pages={3852-3857},
  month = {Oct},
  year = {2019}
  }
  

• @inproceedings{PASSRnet,
  title={Learning parallax attention for stereo image super-resolution},
  author={Wang, Longguang and Wang, Yingqian and Liang, Zhengfa and Lin, Zaiping and Yang, Jungang and An, Wei and Guo, Yulan},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={12250--12259},
  year={2019}
  }
  

The following works have employed the Flickr1024 dataset:

• Stereoscopic Image Super-Resolution with Stereo Consistent Feature, AAAI, 2020. [pdf]
• A Stereo Attention Module for Stereo Image Super-Resolution, IEEE Signal Processing Letters, 2020. [pdf], [code].
• Parallax-based Spatial and Channel Attention for Stereo Image Super-resolution, IEEE Access, 2019. [pdf].
• Convolutional Neural Networks: A Binocular Vision Perspective, arXiv 2019. [pdf]
• Learning Parallax Attention for Stereo Image Super-resolution, CVPR 2019. [pdf], [code].

• The InStereo2K Dataset
• The KITTI Vision Benchmark Suite
• The Middlebury Stereo Vision Page
• The ETH3D Benchmark

Please contact us at [email protected] for any question.