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<center>Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution</center>

<center><a href="https://yingqianwang.github.io" target="_blank">Yingqian Wang</a> <a href="https://longguangwang.github.io/" target="_blank">Longguang Wang</a> Jungang Yang Wei An <a href="http://yulanguo.me/" target="_blank">Yulan Guo</a></center>

<center><img src="pics/Flickr1024.jpg" width="480"></center>

Flickr1024 is a large-scale stereo image dataset which consists of 1024 high-quality image pairs and covers diverse senarios. Details of this dataset can be found in our <a href="http://openaccess.thecvf.com/content_ICCVW_2019/papers/LCI/Wang_Flickr1024_A_Large-Scale_Dataset_for_Stereo_Image_Super-Resolution_ICCVW_2019_paper.pdf">published paper</a>. Although the Flickr1024 dataset was originally developed for stereo image SR (click here for an overview), it was also used for many other tasks such as reference-based SR, stereo matching, and stereo image denoising.

Sample Images

Downloads

The Flickr1024 dataset can be downloaded via <a href="https://pan.baidu.com/s/1YD76gpQ2WjkhjkMnHmU3tQ" target="_blank">Baidu Drive</a> or <a href="https://drive.google.com/file/d/1My6oQaHzclxRrKID-mylvs6Z0d5pT_Cu/view?usp=sharing" target="_blank">Google Drive</a>

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 <a href="https://flickr.com" target="_blank">Flickr</a> 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 <a href="https://www.flickr.com/photos/stereotron/" target="_blank">Sascha Becher</a> and <a href="https://www.flickr.com/photos/tombentz" target="_blank">Tom Bentz</a> 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 = {International Conference on Computer Vision Workshops},
  pages     = {3852-3857},
  month     = {Oct},
  year      = {2019}
  }

Our dataset was used by the following works (selected) for various tasks:

Stereo Image Super-Resolution:

NAFSSR: Stereo Image Super-Resolution Using NAFNet, CVPRW 2022. [<a href="https://arxiv.org/pdf/2204.08714.pdf" target="_blank">pdf</a>]
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results, CVPRW 2022. [<a href="https://arxiv.org/pdf/2204.09197.pdf" target="_blank">pdf</a>]
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation, ACM MM 2021. [<a href="https://arxiv.org/pdf/2106.00985.pdf" target="_blank">pdf</a>]
SA-GNN: Stereo Attention and Graph Neural Network for Stereo Image Super-Resolution, ICIG 2021.
Enhanced Back Projection Network Based Stereo Image Super-Resolution Considering Parallax Attention, ICIP 2021.
Symmetric Parallax Attention for Stereo Image Super-Resolution, CVPRW 2021. [<a href="https://arxiv.org/pdf/2011.03802.pdf" target="_blank">pdf</a>], [<a href="https://github.com/YingqianWang/iPASSR" target="_blank">code</a>], [<a href="https://wyqdatabase.s3-us-west-1.amazonaws.com/iPASSR_visual_comparison.mp4" target="_blank">demo</a>], [<a href="https://wyqdatabase.s3.us-west-1.amazonaws.com/Submission_0021_video_presentation.mp4" target="_blank">presentation</a>].
Cross View Capture for Stereo Image Super-Resolution, TMM 2021. [<a href="https://github.com/xyzhu1/CVCnet" target="_blank">code</a>]
Deep Bilateral Learning for Stereo Image Super-Resolution, IEEE Signal Processing Letters 2021, [<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9382858" target="_blank">pdf</a>].
Parallax-based second-order mixed attention for stereo image super-resolution, IET Computer Vision 2021, [<a href="https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12063" target="_blank">pdf</a>].
A Disparity Feature Alignment Module for Stereo Image Super-Resolution, IEEE Signal Processing Letters 2021.
Deep Stereoscopic Image Super-Resolution via Interaction Module, TCSVT 2020.
Parallax Attention for Unsupervised Stereo Correspondence Learning, TPAMI 2020, [<a href="https://arxiv.org/pdf/2009.08250.pdf" target="_blank">pdf</a>], [<a href="https://github.com/LongguangWang/PAM" target="_blank">code</a>].
Non-Local Nested Residual Attention Network for Stereo Image Super-Resolution, ICASSP 2020, [<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9054687" target="_blank">pdf</a>].
Stereoscopic Image Super-Resolution with Stereo Consistent Feature, AAAI 2020. [<a href="https://aaai.org/ojs/index.php/AAAI/article/view/6880/6734" target="_blank">pdf</a>].
A Stereo Attention Module for Stereo Image Super-Resolution, IEEE Signal Processing Letters 2020. [<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8998204" target="_blank">pdf</a>], [<a href="https://github.com/XinyiYing/SAM" target="_blank">code</a>].
Learning Parallax Attention for Stereo Image Super-resolution, CVPR 2019. [<a href="https://arxiv.org/pdf/1903.05784.pdf" target="_blank">pdf</a>], [<a href="https://github.com/LongguangWang/PASSRnet" target="_blank">code</a>].

Stereo Video Super-Resolution:

Stereo Video Super-Resolution via Exploiting View-Temporal Correlations, ACM MM 2021.

Stereo Matching:

Learning Stereo from Single Images, ECCV 2020, [<a href="https://arxiv.org/pdf/2008.01484.pdf" target="_blank">pdf</a>].

Stereo Image Denoising:

Joint Denoising of Stereo Images Using 3D CNN, ISICV 2020.

Stereo Color Mismatch Correction:

Deep Color Mismatch Correction in Stereoscopic 3D Images, ICIP 2021.

Stereo Color Transfer:

Asymmetric stereo color transfer, ICME 2021.

Reference-based Image Super-Resolution:

Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution, ECCV 2020, [<a href="http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490222.pdf" target="_blank">pdf</a>].

Single Image Denoising:

Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration, arXiv 2020, [<a href="https://arxiv.org/pdf/2006.13714.pdf" target="_blank">pdf</a>].
Image Denoising Using a Novel Deep Generative Network with Multiple Target Images and Adaptive Termination Condition, Applied Science, [<a href="https://www.mdpi.com/2076-3417/11/11/4803/htm" target="_blank">pdf</a>]

Other Tasks:

Reversible data hiding in encrypted images without additional information transmission, Signal Processing: Image Communication 2022.
Adaptive Actor-Critic Bilateral Filter, ICASSP 2022.
IICNet: A Generic Framework for Reversible Image Conversion, ICCV 2021, [<a href="https://arxiv.org/pdf/2109.04242.pdf" target="_blank">pdf</a>], [<a href="https://github.com/felixcheng97/IICNet" target="_blank">code</a>].
Mononizing Binocular Videos, ACM Transactions on Graphics 2020, [<a href="https://arxiv.org/pdf/2009.01424.pdf" target="_blank">pdf</a>].
Convolutional Neural Networks: A Binocular Vision Perspective, arXiv 2019. [<a href="https://arxiv.xilesou.top/pdf/1912.10201.pdf" target="_blank">pdf</a>].
Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset, arXiv 2020, [<a href="https://arxiv.org/pdf/2003.11172.pdf" target="_blank">pdf</a>].

Contact

Any question regarding this work can be addressed to wangyingqian16@nudt.edu.cn.