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Blind Estimation of Motion Blur Parameters for Image Deconvolution

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Pattern Recognition and Image Analysis (IbPRIA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4478))

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Abstract

This paper describes an approach to estimate the parameters of a motion blur (direction and length) directly form the observed image. The motion blur estimate can then be used in a standard non-blind deconvolution algorithm, thus yielding a blind motion deblurring scheme. The estimation criterion is based on recent results about the general spectral behavior of natural images. Experimental results show that the proposed approach is able to accurately estimate both the length and orientation of motion blur kernels, even for small lengths which are traditionally difficult.

This work was supported by Fundação para a Ciência e Tecnologia, under project POSC/EEA-CPS/61271/2004.

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References

  1. Bar, L., Sochen, N.A., Kiryati, N.: Variational pairing of image segmentation and blind restoration. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 166–177. Springer, Heidelberg (2004)

    Google Scholar 

  2. Bioucas-Dias, J.: Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors. IEEE Transactions on Image Processing 15, 937–951 (2006)

    Article  Google Scholar 

  3. Bioucas-Dias, J., Figueiredo, M., Oliveira, J.: Total variation-based image deconvolution: A majorization-minimization approach. In: IEEE International Conference on Acoustics, Speech, and Signal Processing - ICASSP’2006, Toulouse, France (2006)

    Google Scholar 

  4. Bioucas-Dias, J., Figueiredo, M., Oliveira, J.: Adaptive Bayesian/total-variation image deconvolution: A majorization-minimization approach. In: European Signal Processing Conference - EUSIPCO’2006, Florence, Italy (2006)

    Google Scholar 

  5. Bracewell, R.: Two-Dimensional Imaging. Prentice-Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  6. Carasso, A.: Direct blind deconvolution. SIAM Journal of Applied Mathematics 1, 1980–2007 (2001)

    Article  MathSciNet  Google Scholar 

  7. Chan, T., Wong, C.: Total variation blind deconvolution. IEEE Transactions on Image Processing 7, 370–375 (1998)

    Article  Google Scholar 

  8. Combettes, P., Pesquet, J.: Image deconvolution with total variation bounds. In: Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, Paris, France, vol. 1, pp. 441–444 (2003)

    Google Scholar 

  9. Figueiredo, M., Nowak, R.: An EM algorithm for wavelet-based image restoration. IEEE Trans. on Image Processing 12, 906–916 (2003)

    Article  MathSciNet  Google Scholar 

  10. Figueiredo, M., Nowak, R.: A bound optimization approach to wavelet-based image deconvolution. In: IEEE Intern. Conf. on Image Processing – ICIP’05, Genoa, Italy (2005)

    Google Scholar 

  11. Harrington, S.: Computer Graphics: A Programming Approach. McGraw-Hill, New York (1985)

    Google Scholar 

  12. Krahmer, F., Lin, Y., McAdoo, B., Ott, K., Wang, J., Widemannk, D., Wohlberg, B.: Blind image deconvolution: motion blur estimation. Technical Report, Institute of Mathematics and its Applications, University of Minnesota (2006)

    Google Scholar 

  13. Kundur, D., Hatzinakos, D.: Blind image deconvolution. Signal Processing Magazine 13, 43–64 (1996)

    Article  Google Scholar 

  14. Kundur, D., Hatzinakos, D.: Blind image deconvolution revisited. Signal Processing Magazine 13, 61–63 (1996)

    Article  Google Scholar 

  15. Moghaddam, M., Jamzad, M.: Motion blur identification in noisy images using fuzzy sets. In: Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, pp. 862–866 (2005)

    Google Scholar 

  16. Oliveira, J.: Implementation of an exact Radon transform. Technical Report, Instituto de Telecomunicações, Lisboa (2006)

    Google Scholar 

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Joan Martí José Miguel Benedí Ana Maria Mendonça Joan Serrat

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Oliveira, J.P., Figueiredo, M.A.T., Bioucas-Dias, J.M. (2007). Blind Estimation of Motion Blur Parameters for Image Deconvolution. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_76

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  • DOI: https://doi.org/10.1007/978-3-540-72849-8_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72848-1

  • Online ISBN: 978-3-540-72849-8

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