A multi-frame image super-resolution method

Li, XL; Hu, Y; Gao, X; Tao, D; Ning, B

A multi-frame image super-resolution method

Li, XL Hu, Y Gao, X Tao, D

Ning, B

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Publication Type:: Journal Article
Citation:: Signal Processing, 2010, 90 (2), pp. 405 - 414
Issue Date:: 2010-02-01

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Field	Value	Language
dc.contributor.author	Li, XL	en_US
dc.contributor.author	Hu, Y	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	Ning, B	en_US
dc.date.issued	2010-02-01	en_US
dc.identifier.citation	Signal Processing, 2010, 90 (2), pp. 405 - 414	en_US
dc.identifier.issn	0165-1684	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15120
dc.description.abstract	Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction. © 2009 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.sigpro.2009.05.028	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	A multi-frame image super-resolution method	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	90	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	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	90	en_US

Abstract:

Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction. © 2009 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/15120