Temporal Compressive Video Reconstruction Using Gaussian Scale Mixture Model
Authors
Xiao-hai HE, Mao-jiao WANG
Corresponding Author
Xiao-hai HE
Available Online December 2016.
- DOI
- 10.2991/cnct-16.2017.100How to use a DOI?
- Keywords
- Compressive sensing, Video reconstruction, Temporal compressive measurements, Gaussian scale mixture
- Abstract
Compressive sensing has been used to acquire the information in high-frame-rate video using low-frame-rate compressive measurements. Under the framework of coded aperture compressive temporal imaging, we propose a video reconstruction algorithm using Gaussian scale mixture model from temporal compressive measurements. Experimental results demonstrate that our proposed algorithm outperforms state-of-the-art algorithms in both peak signal-to-noise ratio and visual quality.
- Copyright
- © 2017, 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 - Xiao-hai HE AU - Mao-jiao WANG PY - 2016/12 DA - 2016/12 TI - Temporal Compressive Video Reconstruction Using Gaussian Scale Mixture Model BT - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016) PB - Atlantis Press SP - 722 EP - 727 SN - 2352-538X UR - https://doi.org/10.2991/cnct-16.2017.100 DO - 10.2991/cnct-16.2017.100 ID - HE2016/12 ER -