Flickr1024

Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution

Yingqian Wang  Longguang Wang  Jungang Yang  Wei An  Yulan Guo

Flickr1024 is a large-scale stereo dataset, which consists of 1024 high-quality image pairs and covers diverse senarios. [ICCVW paper]

Note: We recommend Awesome-Stereo-Image-SR for an overview of the literature.

Sample Images

Downloads

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

Notations

• 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.

Acknowledgement

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

Citiations

• @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}
  }
  

Related Work

The Flickr1024 dataset is used by the following works:

• Learning Stereo from Single Images, ECCV 2020, [pdf]
• Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration, arXiv 2020, [pdf]
• Non-Local Nested Residual Attention Network for Stereo Image Super-Resolution, ICASSP 2020, [pdf]
• Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset, arXiv 2020, [pdf], [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].

Other Stereo Image Datasets

• The Holopix50k Dataset
• The InStereo2K Dataset
• The WSVD Dataset
• The KITTI Vision Benchmark Suite
• The Middlebury Stereo Vision Page
• The ETH3D Benchmark

Contact

Please contact us at wangyingqian16@nudt.edu.cn for any question.