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Thomas Köhler M. Sc.

Alumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Deblurring for Document Image Restoration

Binarization Driven Blind Deconvolution for Document Image Restoration
Thomas Köhler, Andreas Maier, Vincent Christlein
  • 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 is comparable to state-of-the-art algorithms.

    Supplementary material:

    • The presentation slides for this work are available Opens external link in new windowhere.

    Articles in Conference Proceedings
    Pattern Recognition (37th German Conference on Pattern Recognition, GCPR 2015), RWTH Aachen, 2015, pp. 91-102, 2015, ISBN 978-3-319-24947-6 (BiBTeX, Who cited this?)