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

  • Lei Xiao
  • Jue Wang
  • Wolfgang Heidrich
  • Michael Hirsch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9907)


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.


Text document Camera motion Blind deblurring High-order filters 



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.

Supplementary material

419975_1_En_45_MOESM1_ESM.pdf (2.9 mb)
Supplementary material 1 (pdf 2960 KB)


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Lei Xiao
    • 2
    • 1
  • Jue Wang
    • 3
  • Wolfgang Heidrich
    • 1
    • 2
  • Michael Hirsch
    • 4
  1. 1.KAUSTThuwalSaudi Arabia
  2. 2.University of British ColumbiaVancouverCanada
  3. 3.Adobe ResearchSeattleUSA
  4. 4.MPI for Intelligent SystemsTübingenGermany

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