Zernike moment based image super resolution

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dc.contributor.author Gao, Xinbo en_US
dc.contributor.author Wang, Qian en_US
dc.contributor.author Li, Xuelong en_US
dc.contributor.author Tao, Dacheng en_US
dc.contributor.author Zhang, Kai en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:33:44Z
dc.date.available 2012-10-12T03:33:44Z
dc.date.issued 2011 en_US
dc.identifier 2011001773 en_US
dc.identifier.citation Gao Xinbo et al. 2011, 'Zernike moment based image super resolution', IEEE-Inst Electrical Electronics Engineers Inc, vol. 20, no. 10, pp. 2738-2747. en_US
dc.identifier.issn 1057-7149 en_US
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. en_US
dc.language English en_US
dc.publisher IEEE-Inst Electrical Electronics Engineers Inc en_US
dc.relation.isbasedon en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/TIP.2011.2134859 en_US
dc.title Zernike moment based image super resolution en_US
dc.parent IEEE Transactions On Image Processing en_US
dc.journal.volume 20 en_US
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 080100 en_US
dc.personcode 0000066516 en_US
dc.personcode 0000073864 en_US
dc.personcode 0000073860 en_US
dc.personcode 111502 en_US
dc.personcode 0000032074 en_US
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


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