Big Data Processing With Application to Image Super-Resolution
- 10.2991/itms-15.2015.187How to use a DOI?
- Face hallucination; Position-patch; Matrix regression
Learning based face hallucination methods have received much attention in recent years. As opposed to the existing methods, where the input image (patch) matrix is first converted into vectors before combination coefficients calculation, this paper proposes a novel matrix based regression model for directly combination coefficients calculation to preserve the structural information of the input matrix. For each low-resolution local patch matrix, its combination coefficients over the same position image patch matrices in training images can be computed. Then the corresponding high-resolution patch matrix can be obtained. Experiments conducted on the FERET face dataset indicate that our method could outperform other state-of-the-art algorithms in terms of both vision and quantity.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiangjun Meng AU - Baiqing Diao AU - Lipeng Zhu AU - Guangwei Gao AU - Song Deng PY - 2015/11 DA - 2015/11 TI - Big Data Processing With Application to Image Super-Resolution BT - Proceedings of the 2015 International Conference on Industrial Technology and Management Science PB - Atlantis Press SP - 791 EP - 794 SN - 2352-538X UR - https://doi.org/10.2991/itms-15.2015.187 DO - 10.2991/itms-15.2015.187 ID - Meng2015/11 ER -