Zernike-moment-based image super resolution.

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dc.contributor.author Gao, X
dc.contributor.author Wang, Q
dc.contributor.author Li, X
dc.contributor.author Tao, D
dc.contributor.author Zhang, K
dc.date.accessioned 2012-10-12T03:33:44Z
dc.date.issued 2011-10
dc.identifier.citation IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2011, 20 (10), pp. 2738 - 2747
dc.identifier.issn 1057-7149
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18256
dc.description.abstract Multiframe super-resolution (SR) reconstruction aims to produce a high-resolution (HR) image using a set of low-resolution (LR) images. In the process of reconstruction, fuzzy registration usually plays a critical role. It mainly focuses on the correlation between pixels of the candidate and the reference images to reconstruct each pixel by averaging all its neighboring pixels. Therefore, the fuzzy-registration-based SR performs well and has been widely applied in practice. However, if some objects appear or disappear among LR images or different angle rotations exist among them, the correlation between corresponding pixels becomes weak. Thus, it will be difficult to use LR images effectively in the process of SR reconstruction. Moreover, if the LR images are noised, the reconstruction quality will be affected seriously. To address or at least reduce these problems, this paper presents a novel SR method based on the Zernike moment, to make the most of possible details in each LR image for high-quality SR reconstruction. Experimental results show that the proposed method outperforms existing methods in terms of robustness and visual effects.
dc.format Print
dc.language eng
dc.relation.isbasedon 10.1109/tip.2011.2134859
dc.title Zernike-moment-based image super resolution.
dc.type Journal Article
dc.parent IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
dc.journal.volume 10
dc.journal.volume 20
dc.journal.number 10 en_US
dc.publocation Piscataway en_US
dc.identifier.startpage 2738 en_US
dc.identifier.endpage 2747 en_US
dc.cauo.name FEIT.A/DRsch Ctr Quantum Computat'n & Intelligent Systs en_US
dc.conference Verified OK en_US
dc.for 0906 Electrical and Electronic Engineering
dc.for 0801 Artificial Intelligence and Image Processing
dc.for 1702 Cognitive Sciences
dc.personcode 111502
dc.percentage 34 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Fuzzy motion estimation, image super resolution (SR), Zernike moment en_US
pubs.embargo.period Not known
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/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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