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Blur Identification and Image Restoration Based on Evolutionary Multiple Object Segmentation for Digital Auto-focusing

  • Jeongho Shin
  • Sunghyun Hwang
  • Kiman Kim
  • Jinyoung Kang
  • Seongwon Lee
  • Joonki Paik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3322)

Abstract

This paper presents a digital auto-focusing algorithm based on evolutionary multiple object segmentation method. Robust object segmentation can be conducted by the evolutionary algorithm on an image that has several differently out-of-focused objects. After segmentation is completed, point spread functions (PSFs) are estimated at differently out-of-focused objects and spatially adaptive image restorations are applied according to the estimated PSFs. Experimental results show that the proposed auto-focusing algorithm can efficiently remove the space-variant out-of-focus blur from the image with multiple, blurred objects.

Keywords

Evolutionary Algorithm Concentric Circle Image Restoration Edge Point Active Contour Model 
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.
    Andrews, H.C., Hunt, B.R.: Digital Image Restoration. Prentice-Hall, New Jersey (1977)Google Scholar
  2. 2.
    Subbarao, M., Tyan, J.K.: Selecting the optimal focus measure for autofocusing and depth-from-focus. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 864–870 (1998)CrossRefGoogle Scholar
  3. 3.
    Kass, M., Witzkin, A., Terzopoulos, D.: Snake: Active contour model. International Journal of Computer Vision, 321–331 (1988)Google Scholar
  4. 4.
    Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)Google Scholar
  5. 5.
    Goldberg, D.: Genetic Algorithm in Search, Optimization and Machine Learning. Addision-Wesley, London (1989)Google Scholar
  6. 6.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. PWS Publishing (1999)Google Scholar
  7. 7.
    Kim, S.K., Park, S.R., Paik, J.K.: Simultaneous out-of-focus blur estimation and restoration for digital auto-focusing system. IEEE Trans. Consumer Electronics 34, 1071–1075 (1998)Google Scholar
  8. 8.
    Lagendijk, R.L., Biemond, J., Boekee, D.E.: Identification and restoration of noisy blurred image using the expectation-maximization algorithm. IEEE Trans. Acoustic, Speech and Signal Processing 38, 1180–1191 (1990)zbMATHCrossRefGoogle Scholar
  9. 9.
    Reeves, S.J., Mersereau, M.R.: Blue identification by the method of generalized cross-validation. IEEE Trans. Image Processing 1, 301–311 (1992)CrossRefGoogle Scholar
  10. 10.
    Lun, D.P.K., Chan, T.C.L., Hsung, T.C., Feng, D.D., Chan, Y.H.: Efficient blind restoration using discrete periodic radon transform. IEEE Trans. Image Processing 13, 188–200 (2004)CrossRefGoogle Scholar
  11. 11.
    Noble, B., Daniel, J.: Applied Linear Algebra. Prentice-Hall, Englewood Cliffs (1988)Google Scholar
  12. 12.
    Katsaggelos, A.K.: Iterative image restoration algorithms. Optical Engineering 287, 735–748 (1989)Google Scholar
  13. 13.
    Miller, K.: Least-squares method for ill-posed problems with a prescribed bound. SIAM J. Math. Anal. 1, 52–57 (1970)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Pratt, W.K.: Digital Image Processing, 2nd edn. John Wiley, Chichester (1991)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jeongho Shin
    • 1
  • Sunghyun Hwang
    • 1
  • Kiman Kim
    • 1
  • Jinyoung Kang
    • 1
  • Seongwon Lee
    • 1
  • Joonki Paik
    • 1
  1. 1.Image Processing and Intelligent Systems Lab, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and FilmChung-Ang UniversitySeoulKorea

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