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Abstract

A blind image deblurring method based on a new non-gaussianity measure and independent component analysis is presented. The scheme assumes independency among source signals (image and filter function) in the frequency domain. According to the Central Limit Theorem the blurred image becomes more Gaussian. The original image is assumed to be non-gaussian and using a spectral non-gaussianity measure (kurtosis or negentropy) one can estimate an inverse filter function that maximizes the non-gaussianity of the deblurred image. A genetic algorithm (GA) optimizing the kurtosis in the frequency domain is used for the deblurring process. Experimental results are presented and compared with some existing methods. The results show that the deblurring from the spectral domain offers several advantages over that from the spatial domain.

Keywords

Independent Component Analysis Point Spread Function Independent Component Analysis Motion Blur Blind Deconvolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Oppenheim, A.V., Schafer, R.W., Stockham, T.G.: Nonlinear Filtering Of Multiplied and Convolved Signals. Proceedings of the Institute of Electrical and Electronics Engineers 56, 1264 (1968)CrossRefGoogle Scholar
  2. 2.
    Stockham, T.G., Cannon, T.M., Ingebretsen, R.B.: Blind Deconvolution through Digital Signal-Processing. Proc. IEEE 63, 678–692 (1975)CrossRefGoogle Scholar
  3. 3.
    Richardson, W.H.: Bayesian-Based Iterative Method of Image Restoration. Journal of the Optical Society of America 62, 55 (1972)CrossRefGoogle Scholar
  4. 4.
    Chan, T.F., Wong, C.K.: Total Variation Blind Deconvolution. IEEE Trans. Image Process. 7, 370–375 (1998)CrossRefGoogle Scholar
  5. 5.
    Wiggins, R.A.: Minimum Entropy Deconvolution. Geoexploration 16, 21–35 (1978)CrossRefGoogle Scholar
  6. 6.
    Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience Publication, Hoboken (2001)CrossRefGoogle Scholar
  7. 7.
    Comon, P., Jutten, C.: Handbook of Blind Source Separation (Independent Component Analysis and Applications). Elsevier, Amsterdam (2010)Google Scholar
  8. 8.
    Yin, H.J., Hussain, I.: Independent Component Analysis and Non-Gaussianity for Blind Image Deconvolution and Deblurring. Integr. Comput.-Aided Eng. 15, 219–228 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aftab Khan
    • 1
  • Hujun Yin
    • 1
  1. 1.The University of ManchesterUK

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