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Binarization Driven Blind Deconvolution for Document Image Restoration

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

Blind deconvolution is a common method for restoration of blurred text images, while binarization is employed to analyze and interpret the text semantics. In literature, these tasks are typically treated independently. This paper introduces a novel binarization driven blind deconvolution approach to couple both tasks in a common framework. The proposed method is derived as an energy minimization problem regularized by a novel consistency term to exploit text binarization as a prior for blind deconvolution. The binarization to establish our consistency term is inferred by spatially regularized soft clustering based on a set of discriminative features. Our algorithm is formulated by the alternating direction method of multipliers and iteratively refines blind deconvolution and binarization. In our experimental evaluation, we show that our joint framework is superior to treating binarization and deconvolution as independent subproblems. We also demonstrate the application of our method for the restoration and binarization of historic document images, where it improves the visual recognition of handwritten text.

This work is partly supported by the German Federal Ministry of Education and Research (BMBF), grant-nr. 01UG1236a

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Authors and Affiliations

  1. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Thomas Köhler, Andreas Maier & Vincent Christlein

  2. Erlangen Graduate School in Advanced Optical Technologies (SAOT), Erlangen, Germany

    Thomas Köhler & Andreas Maier

Authors
  1. Thomas Köhler
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  2. Andreas Maier
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  3. Vincent Christlein
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Corresponding author

Correspondence to Thomas Köhler .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Köhler, T., Maier, A., Christlein, V. (2015). Binarization Driven Blind Deconvolution for Document Image Restoration. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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