Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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
https://doi.org/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/).

Download article (PDF)

Volume Title
Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
Series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-301-2
ISSN
2352-538X
DOI
https://doi.org/10.2991/cnct-16.2017.100How to use a DOI?
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  - https://doi.org/10.2991/cnct-16.2017.100
ID  - HE2016/12
ER  -