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    Zernike-moment-based image super resolution

    Gao, X. and Wang, Q. and Li, Xuelong and Tao, D. and Zhang, K. (2011) Zernike-moment-based image super resolution. IEEE Transactions on Image Processing 20 (10), pp. 2738-2747. ISSN 1057-7149.

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    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.


    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 07 Jun 2013 13:19
    Last Modified: 11 Oct 2016 15:27


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