Super-resolution by prediction based sub-pel motion estimation

Mudugamuwa, DJ; He, X; Wei, D; Ahn, CH

Super-resolution by prediction based sub-pel motion estimation

Mudugamuwa, DJ He, X

Wei, D Ahn, CH

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Publication Type:: Conference Proceeding
Citation:: 2009 24th International Conference Image and Vision Computing New Zealand, IVCNZ 2009 - Conference Proceedings, 2009, pp. 282 - 287
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Mudugamuwa, DJ	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Wei, D	en_US
dc.contributor.author	Ahn, CH	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	2009 24th International Conference Image and Vision Computing New Zealand, IVCNZ 2009 - Conference Proceedings, 2009, pp. 282 - 287	en_US
dc.identifier.isbn	9781424446988	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11884
dc.description.abstract	Super-resolution reconstruction produces high-resolution images from a set of low-resolution images of the same scene. In the last two and a half decades, many super-resolution algorithms have been proposed. These algorithms are usually very sensitive to their assumed models of data and noise, and also computationally expensive for many practical applications. In this paper we adopt computationally efficient prediction based sub-pel motion estimation to produce a fast super-resolution reconstruction that can also accommodate generic motion patterns. The proposed algorithm adaptively exploits the available high frequency content in adjacent video frames to generate high resolution video frames. Initial experiments showed promising results of around 2dB, PSNR improvement, over single frame bi-linear interpolation. © 2009 IEEE.	en_US
dc.relation.ispartof	2009 24th International Conference Image and Vision Computing New Zealand, IVCNZ 2009 - Conference Proceedings	en_US
dc.relation.isbasedon	10.1109/IVCNZ.2009.5378397	en_US
dc.title	Super-resolution by prediction based sub-pel motion estimation	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Wellington, New Zealand	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Super-resolution reconstruction produces high-resolution images from a set of low-resolution images of the same scene. In the last two and a half decades, many super-resolution algorithms have been proposed. These algorithms are usually very sensitive to their assumed models of data and noise, and also computationally expensive for many practical applications. In this paper we adopt computationally efficient prediction based sub-pel motion estimation to produce a fast super-resolution reconstruction that can also accommodate generic motion patterns. The proposed algorithm adaptively exploits the available high frequency content in adjacent video frames to generate high resolution video frames. Initial experiments showed promising results of around 2dB, PSNR improvement, over single frame bi-linear interpolation. © 2009 IEEE.

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http://hdl.handle.net/10453/11884