Image super-resolution via non-local steering kernel regression regularization

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

Image super-resolution via non-local steering kernel regression regularization

Zhang, K Gao, X Tao, D

Li, X

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Publication Type:: Conference Proceeding
Citation:: 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 943 - 946
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Zhang, K	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013, pp. 943 - 946	en_US
dc.identifier.isbn	9781479923410	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28031
dc.description.abstract	In this paper, we employ the non-local steering kernel regression to construct an effective regularization term for the single image super-resolution problem. The proposed method seamlessly integrates the properties of local structural regularity and non-local self-similarity existing in natural images, and solves a least squares minimization problem for obtaining the desired high-resolution image. Extensive experimental results on both simulated and real low-resolution images demonstrate that the proposed method can restore compelling results with sharp edges and fine textures. © 2013 IEEE.	en_US
dc.relation.ispartof	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICIP.2013.6738195	en_US
dc.title	Image super-resolution via non-local steering kernel regression regularization	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne, Australia	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:

In this paper, we employ the non-local steering kernel regression to construct an effective regularization term for the single image super-resolution problem. The proposed method seamlessly integrates the properties of local structural regularity and non-local self-similarity existing in natural images, and solves a least squares minimization problem for obtaining the desired high-resolution image. Extensive experimental results on both simulated and real low-resolution images demonstrate that the proposed method can restore compelling results with sharp edges and fine textures. © 2013 IEEE.

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http://hdl.handle.net/10453/28031