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Introducing Dynamic Prior Knowledge to Partially-Blurred Image Restoration

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Pattern Recognition (DAGM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4174))

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Abstract

The paper presents an unsupervised method for partially-blurred image restoration without influencing unblurred regions or objects. Maximum a posteriori estimation of parameters in Bayesian regularization is equal to minimizing energy of a dataset for a given number of classes. To estimate the point spread function (PSF), a parametric model space is introduced to reduce the searching uncertainty for PSF model selection. Simultaneously, PSF self-initializing does not rely on supervision or thresholds. In the image domain, a gradient map as a priori knowledge is derived not only for dynamically choosing nonlinear diffusion operators but also for segregating blurred and unblurred regions via an extended graph-theoretic method. The cost functions with respect to the image and the PSF are alternately minimized in a convex manner. The algorithm is robust in that it can handle images that are formed in variational environments with different blur and stronger noise.

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Author information

Authors and Affiliations

  1. Computer Vision & Remote Sensing, Berlin University of Technology, Franklinstrasse 28/29, Office FR 3-1, D-10587, Berlin

    Hongwei Zheng & Olaf Hellwich

Authors
  1. Hongwei Zheng
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  2. Olaf Hellwich
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Editor information

Editors and Affiliations

  1. Norwegian Information Security Laboratory, Gjøvik University College, Norway

    Katrin Franke

  2. Fraunhofer FIRST (IDA), Berlin, Germany

    Klaus-Robert Müller

  3. Department of Security Technology, Fraunhofer Institute for Production Systems and Design Technology (IPK), Pascalstr. 8-9, 10587, Berlin, Germany

    Bertram Nickolay

  4. Department of Electronic Imaging Technology, Fraunhofer Institute for Information and Communication Technology, Heinrich Hertz Institute (HHI), Einsteinufer 37, 10587, Berlin, Germany

    Ralf Schäfer

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© 2006 Springer-Verlag Berlin Heidelberg

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Zheng, H., Hellwich, O. (2006). Introducing Dynamic Prior Knowledge to Partially-Blurred Image Restoration. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_12

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  • DOI: https://doi.org/10.1007/11861898_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44412-1

  • Online ISBN: 978-3-540-44414-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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