Efficient super-resolution by finer sub-pixel motion prediction and bilateral filtering

Mudugamuwa, DJ; He, X; Jia, W

Efficient super-resolution by finer sub-pixel motion prediction and bilateral filtering

Mudugamuwa, DJ He, X

Jia, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Multimedia and Expo, 2012, pp. 800 - 805
Issue Date:: 2012-11-05

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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	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.date.issued	2012-11-05	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Multimedia and Expo, 2012, pp. 800 - 805	en_US
dc.identifier.issn	1945-7871	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22977
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 very sensitive to their assumed models of motion and noise, and computationally expensive for many practical applications. In this paper we adopt earlier reported fast prediction based sub-pixel motion estimation and a novel interpolation scheme based on the bilateral filter to produce a fast color super-resolution reconstruction that can accommodate arbitrary local motion patterns. The proposed algorithm exploits photometric proximity and available finer fractional motion information in the high resolution grid, to reconstruct enhanced super-resolved image frames. Experiments show a PSNR performance comparable to the state-of-the-art but at a fraction of their computational cost. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Multimedia and Expo	en_US
dc.relation.isbasedon	10.1109/ICME.2012.103	en_US
dc.title	Efficient super-resolution by finer sub-pixel motion prediction and bilateral filtering	en_US
dc.type	Conference Proceeding
utslib.for	080106 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
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/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 very sensitive to their assumed models of motion and noise, and computationally expensive for many practical applications. In this paper we adopt earlier reported fast prediction based sub-pixel motion estimation and a novel interpolation scheme based on the bilateral filter to produce a fast color super-resolution reconstruction that can accommodate arbitrary local motion patterns. The proposed algorithm exploits photometric proximity and available finer fractional motion information in the high resolution grid, to reconstruct enhanced super-resolved image frames. Experiments show a PSNR performance comparable to the state-of-the-art but at a fraction of their computational cost. © 2012 IEEE.

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http://hdl.handle.net/10453/22977