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.
This work was supported in part by Korean Ministry of Science and Technology under the National Research Lab. Project, in part by Korean Ministry of Education under Brain Korea 21 Project, and in part by grant No.R08-2004-000-10626-0 from the Basic Research Program of the Korea Science & Engineering Foundation.
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References
Andrews, H.C., Hunt, B.R.: Digital Image Restoration. Prentice-Hall, New Jersey (1977)
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)
Kass, M., Witzkin, A., Terzopoulos, D.: Snake: Active contour model. International Journal of Computer Vision, 321–331 (1988)
Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)
Goldberg, D.: Genetic Algorithm in Search, Optimization and Machine Learning. Addision-Wesley, London (1989)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. PWS Publishing (1999)
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)
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)
Reeves, S.J., Mersereau, M.R.: Blue identification by the method of generalized cross-validation. IEEE Trans. Image Processing 1, 301–311 (1992)
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)
Noble, B., Daniel, J.: Applied Linear Algebra. Prentice-Hall, Englewood Cliffs (1988)
Katsaggelos, A.K.: Iterative image restoration algorithms. Optical Engineering 287, 735–748 (1989)
Miller, K.: Least-squares method for ill-posed problems with a prescribed bound. SIAM J. Math. Anal. 1, 52–57 (1970)
Pratt, W.K.: Digital Image Processing, 2nd edn. John Wiley, Chichester (1991)
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Shin, J., Hwang, S., Kim, K., Kang, J., Lee, S., Paik, J. (2004). Blur Identification and Image Restoration Based on Evolutionary Multiple Object Segmentation for Digital Auto-focusing. In: Klette, R., Žunić, J. (eds) Combinatorial Image Analysis. IWCIA 2004. Lecture Notes in Computer Science, vol 3322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30503-3_50
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DOI: https://doi.org/10.1007/978-3-540-30503-3_50
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