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    Enhancing Bayesian estimators for removing camera shake

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    Authors
    Wang, Chao
    Yue, Y.
    Dong, Feng
    Tao, Yubo
    Ma, Xiangyin
    Clapworthy, Gordon J.
    Ye, Xujiong
    Affiliation
    University of Bedfordshire
    Issue Date
    2013
    Subjects
    blind deconvolution
    Bayesian estimator
    image deblurring
    image processing
    sharpening
    computer vision
    deblurring
    
    Metadata
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    Abstract
    The aim of removing camera shake is to estimate a sharp version x from a shaken image y when the blur kernel k is unknown. Recent research on this topic evolved through two paradigms called MAP(k) and MAP(x,k). MAP(k) only solves for k by marginalizing the image prior, while MAP(x,k) recovers both x and k by selecting the mode of the posterior distribution. This paper first systematically analyses the latent limitations of these two estimators through Bayesian analysis. We explain the reason why it is so difficult for image statistics to solve the previously reported MAP(x,k) failure. Then we show that the leading MAP(x,k) methods, which depend on efficient prediction of large step edges, are not robust to natural images due to the diversity of edges. MAP(k), although much more robust to diverse edges, is constrained by two factors: the prior variation over different images, and the ratio between image size and kernel size. To overcome these limitations, we introduce an inter-scale prior prediction scheme and a principled mechanism for integrating the sharpening filter into MAP(k). Both qualitative results and extensive quantitative comparisons demonstrate that our algorithm outperforms state-of-the-art methods.
    Citation
    Wang, C., Yue, Y., Dong, F., Tao, Y., Ma, X., Clapworthy, G.J. (2013) 'Enhancing Bayesian Estimators for Removing Camera Shake', Computer Graphics Forum, 32 (6) pp113-125.
    Publisher
    Wiley
    Journal
    Computer Graphics Forum
    URI
    http://hdl.handle.net/10547/337005
    DOI
    10.1111/cgf.12074
    Additional Links
    http://doi.wiley.com/10.1111/cgf.12074
    Type
    Article
    Language
    en
    ISSN
    0167-7055
    ae974a485f413a2113503eed53cd6c53
    10.1111/cgf.12074
    Scopus Count
    Collections
    Centre for Computer Graphics and Visualisation (CCGV)

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