Learning priors for super-resolution in video sequence

Peng, Y; Jin, JS; Luo, S; Xu, M

Learning priors for super-resolution in video sequence

Peng, Y Jin, JS Luo, S Xu, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10, 2010, pp. 163 - 166
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Peng, Y	en_US
dc.contributor.author	Jin, JS	en_US
dc.contributor.author	Luo, S	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10, 2010, pp. 163 - 166	en_US
dc.identifier.isbn	9781450304603	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16245
dc.description.abstract	Video becomes a crucial information resource in last decades, because of the rapid development of camera as well as the internet explosion. High-quality video sequences are always desired in lots of fields. Since the bottleneck of data storage and interferences of shooting condition, we cannot always obtain high-resolution video. This botheration can be circumvented by super-resolution. Currently, almost super-resolution techniques are in the framework of Maximum a Posterior (MAP). Appropriate parameters of prior distribution are crucial for recovering accurate super-resolution image. We utilise a novel Weighted Cross Validation (WCG) method to learn theses prior parameters. Comparison experiments are provided to illustrate the effectiveness of our approach. Copyright 2010 ACM.	en_US
dc.relation.ispartof	Proceedings of the 2nd International Conference on Internet Multimedia Computing and Service, ICIMCS'10	en_US
dc.relation.isbasedon	10.1145/1937728.1937767	en_US
dc.title	Learning priors for super-resolution in video sequence	en_US
dc.type	Conference Proceeding
utslib.for	080305 Multimedia Programming	en_US
dc.location.activity	Harbin, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Video becomes a crucial information resource in last decades, because of the rapid development of camera as well as the internet explosion. High-quality video sequences are always desired in lots of fields. Since the bottleneck of data storage and interferences of shooting condition, we cannot always obtain high-resolution video. This botheration can be circumvented by super-resolution. Currently, almost super-resolution techniques are in the framework of Maximum a Posterior (MAP). Appropriate parameters of prior distribution are crucial for recovering accurate super-resolution image. We utilise a novel Weighted Cross Validation (WCG) method to learn theses prior parameters. Comparison experiments are provided to illustrate the effectiveness of our approach. Copyright 2010 ACM.

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