Wisdom of crowds: Single image super-resolution from the Web

Li, J; Tao, D

Wisdom of crowds: Single image super-resolution from the Web

Li, J

Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Data Mining, ICDM, 2011, pp. 812 - 816
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Li, J https://orcid.org/0000-0002-1336-2241	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Data Mining, ICDM, 2011, pp. 812 - 816	en_US
dc.identifier.isbn	9780769544090	en_US
dc.identifier.issn	1550-4786	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19128
dc.description.abstract	This paper addresses the problem of learning based single image super-resolution. Previous research on this problem employs human user to provide a set of images that are similar to the target image as a reference. Then the superresolution algorithm can learn from the provided reference images to predict the high resolution details for the target image. We propose a fully automatic scheme, which leverages the knowledge of the entire visual world and to query relevant references from the Internet. The proposed scheme is free of human supervision, and the performance compromise is small. We conduct experiments to show the effectiveness of the method. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Data Mining, ICDM	en_US
dc.relation.isbasedon	10.1109/ICDMW.2011.179	en_US
dc.title	Wisdom of crowds: Single image super-resolution from the Web	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Vancouver, Canada	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper addresses the problem of learning based single image super-resolution. Previous research on this problem employs human user to provide a set of images that are similar to the target image as a reference. Then the superresolution algorithm can learn from the provided reference images to predict the high resolution details for the target image. We propose a fully automatic scheme, which leverages the knowledge of the entire visual world and to query relevant references from the Internet. The proposed scheme is free of human supervision, and the performance compromise is small. We conduct experiments to show the effectiveness of the method. © 2011 IEEE.

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http://hdl.handle.net/10453/19128