Combining Exemplar-Based Approach and Learning-Based Approach for Light Field Super-Resolution Using a Hybrid Imaging System
Abstract
We propose a new method to super-resolve images captured by a hybrid light field system that consists of a standard light field camera and a high-resolution standard camera. The high-resolution image is taken as a reference to help with super-resolving the low-resolution light field images. Our method combines an exemplar-based algorithm with the state of-the-art single image super-resolution approach and draws on the strengths of both. Both quantitative and qualitative experiments show that our proposed method substantially outperforms existing methods on standard light field datasets in the challenging large parallax setting.
Cite
Text
Zheng et al. "Combining Exemplar-Based Approach and Learning-Based Approach for Light Field Super-Resolution Using a Hybrid Imaging System." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.292Markdown
[Zheng et al. "Combining Exemplar-Based Approach and Learning-Based Approach for Light Field Super-Resolution Using a Hybrid Imaging System." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/zheng2017iccvw-combining/) doi:10.1109/ICCVW.2017.292BibTeX
@inproceedings{zheng2017iccvw-combining,
title = {{Combining Exemplar-Based Approach and Learning-Based Approach for Light Field Super-Resolution Using a Hybrid Imaging System}},
author = {Zheng, Haitian and Guo, Minghao and Wang, Haoqian and Liu, Yebin and Fang, Lu},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {2481-2486},
doi = {10.1109/ICCVW.2017.292},
url = {https://mlanthology.org/iccvw/2017/zheng2017iccvw-combining/}
}