Partially supervised neighbor embedding for example-based image super-resolution

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

Partially supervised neighbor embedding for example-based image super-resolution

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

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Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Topics in Signal Processing, 2011, 5 (2), pp. 230 - 239
Issue Date:: 2011-04-01

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Field	Value	Language
dc.contributor.author	Zhang, K	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2011-04-01	en_US
dc.identifier.citation	IEEE Journal on Selected Topics in Signal Processing, 2011, 5 (2), pp. 230 - 239	en_US
dc.identifier.issn	1932-4553	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15448
dc.description.abstract	Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding. © 2010 IEEE.	en_US
dc.relation.ispartof	IEEE Journal on Selected Topics in Signal Processing	en_US
dc.relation.isbasedon	10.1109/JSTSP.2010.2048606	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Partially supervised neighbor embedding for example-based image super-resolution	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	5	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1005 Communications Technologies	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.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	5	en_US

Abstract:

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding. © 2010 IEEE.

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http://hdl.handle.net/10453/15448