Single image super resolution with high resolution dictionary

Mu, G; Gao, X; Zhang, K; Li, X; Tao, D

Single image super resolution with high resolution dictionary

Mu, G Gao, X Zhang, K Li, X Tao, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2011, pp. 1141 - 1144
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Mu, G	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Zhang, K	en_US
dc.contributor.author	Li, X	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 - International Conference on Image Processing, ICIP, 2011, pp. 1141 - 1144	en_US
dc.identifier.isbn	9781457713033	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120477
dc.description.abstract	Image super resolution (SR) is a technique to estimate or synthesize a high resolution (HR) image from one or several low resolution (LR) images. This paper proposes a novel framework for single image super resolution based on sparse representation with high resolution dictionary. Unlike the previous methods, the training set is constructed from the HR images instead of HR-LR image pairs. Due to this property, there is no need to retrain a new dictionary when the zooming factor changed. Given a testing LR image, the patch-based representation coefficients and the desired image are estimated alternately through the use of dynamic group sparsity, the fidelity term and the non-local means regularization. Experimental results demonstrate the effectiveness of the proposed algorithm. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2011.6115630	en_US
dc.title	Single image super resolution with high resolution dictionary	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Image super resolution (SR) is a technique to estimate or synthesize a high resolution (HR) image from one or several low resolution (LR) images. This paper proposes a novel framework for single image super resolution based on sparse representation with high resolution dictionary. Unlike the previous methods, the training set is constructed from the HR images instead of HR-LR image pairs. Due to this property, there is no need to retrain a new dictionary when the zooming factor changed. Given a testing LR image, the patch-based representation coefficients and the desired image are estimated alternately through the use of dynamic group sparsity, the fidelity term and the non-local means regularization. Experimental results demonstrate the effectiveness of the proposed algorithm. © 2011 IEEE.

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http://hdl.handle.net/10453/120477