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Double Regularized Bayesian Estimation for Blur Identification in Video Sequences

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Computer Vision – ACCV 2006 (ACCV 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3852))

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Abstract

Blind blur identification in video sequences becomes more important. This paper presents a new method for identifying parameters of different blur kernels and image restoration in a weighted double regularized Bayesian learning approach. A proposed prior solution space includes dominant blur point spread functions as prior candidates for Bayesian estimation. The double cost functions are adjusted in a new alternating minimization approach which successfully computes the convergence for a number of parameters. The discussion of choosing regularization parameters for both image and blur function is also presented. The algorithm is robust in that it can handle images that are formed in variational environments with different types of blur. Numerical tests show that the proposed algorithm works effectively and efficiently in practical applications.

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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. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    P. J. Narayanan

  2. Department of Computer Science, Columbia University, 500 West 120th Street, NY 10027, New York, USA

    Shree K. Nayar

  3. Microsoft Research Asia, P.O. Box, Beijing, P.R. China

    Heung-Yeung Shum

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© 2006 Springer-Verlag Berlin Heidelberg

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Zheng, H., Hellwich, O. (2006). Double Regularized Bayesian Estimation for Blur Identification in Video Sequences. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_94

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  • DOI: https://doi.org/10.1007/11612704_94

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31244-4

  • Online ISBN: 978-3-540-32432-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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