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README.md

With recent advances in camera manufacturing, light field (LF) imaging technology becomes increasingly popular and is commonly used in various applications such as mobile phones, biological microscope, VR/AR etc. However, LF images captured by different devices (especially camera arrays) usually have significantly different baseline lengths. It is therefore, necessary to know how existing LF algorithms work under baseline variations, including those developed for depth estimation, view synthesis, and image SR. To facilitate the study of LF algorithms under baseline variations, in this repository, we introduce a novel LF dataset (namely, the NUDT dataset) with adjustable baselines.

Training Set

Coming soon.

Test Set

bedroom chess robot study
  • Note: click the scene name to download the dataset.

Citiation

If you find this work helpful, please consider citing the following paper:

@article{LF-DFnet,
  title={Light Field Image Super-Resolution Using Deformable Convolution},
  author={Wang, Yingqian and Yang, Jungang and Wang, Longguang and Ying, Xinyi and Wu, Tianhao and An, Wei and Guo, Yulan},
  journal={arXiv preprint arXiv:2007.03535},
  year={2020}
}

Contact

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

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