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    Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs

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    Type
    Book Chapter
    Authors
    Xiao, Lei
    Wang, Jue
    Heidrich, Wolfgang cc
    Hirsch, Michael
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2016-09-17
    Online Publication Date
    2016-09-17
    Print Publication Date
    2016
    Permanent link to this record
    http://hdl.handle.net/10754/622213
    
    Metadata
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    Abstract
    Photographs of text documents taken by hand-held cameras can be easily degraded by camera motion during exposure. In this paper, we propose a new method for blind deconvolution of document images. Observing that document images are usually dominated by small-scale high-order structures, we propose to learn a multi-scale, interleaved cascade of shrinkage fields model, which contains a series of high-order filters to facilitate joint recovery of blur kernel and latent image. With extensive experiments, we show that our method produces high quality results and is highly efficient at the same time, making it a practical choice for deblurring high resolution text images captured by modern mobile devices. © Springer International Publishing AG 2016.
    Citation
    Xiao L, Wang J, Heidrich W, Hirsch M (2016) Learning High-Order Filters for Efficient Blind Deconvolution of Document Photographs. Lecture Notes in Computer Science: 734–749. Available: http://dx.doi.org/10.1007/978-3-319-46487-9_45.
    Sponsors
    This work was supported in part by Adobe and Baseline Funding of KAUST. Part of this work was done when the first author was an intern at Adobe Research. The authors thank the anonymous reviewers for helpful suggestions.
    Publisher
    Springer Nature
    Journal
    Lecture Notes in Computer Science
    DOI
    10.1007/978-3-319-46487-9_45
    Additional Links
    http://link.springer.com/chapter/10.1007%2F978-3-319-46487-9_45
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-46487-9_45
    Scopus Count
    Collections
    Computer Science Program; Visual Computing Center (VCC); Book Chapters; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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