Abstract
This paper proposes a novel digital auto-focusing algorithm using image fusion, which restores an out-of-focus image with multiple, differently out-of-focus objects. The proposed auto-focusing algorithm consists of (i) building a prior set of point spread functions (PSFs), (ii) image restoration, and (iii) fusion of the restored images. Instead of designing an image restoration filter for multi-object auto-focusing, we propose an image fusion-based auto-focusing algorithm by fusing multiple, restored images based on prior estimated set of PSFs. The prior estimated PSFs overcome heavy computational overhead and make the algorithm suitable for real-time applications. By utilizing both redundant and complementary information provided by different images, the proposed fusion algorithm can restore images with multiple, out-of-focus objects. Experimental results show the performance of the proposed auto-focusing algorithm.
This work was supported by Korean Ministry of Science and Technology under the National Research Laboratory Project, by Korean Ministry of Information and Communication under the Chung-Ang University HNRC-ITRC program, and by the Korea Research Foundation Grant funded by Korean Government (MOEHRD)(R08-2004-000-10626-0).
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References
Andrews, H.C., Hunt, B.R.: Digital Image Restoration. Prentice-Hall, New Jersey (1977)
Kim, S.K., Park, S.R., Paik, J.K.: Simultaneous Out-of-Focus Blur Estimation and Restoration for Digital AF System. IEEE Trans. Consumer Electronics 44(3), 1071–1075 (1998)
Tekalp, M., Kaufman, H., Woods, J.W.: Identification of Image and Blur Parameters for the Restoration of Noncausal Blurs. In: IEEE Trans. Acoustics, Speech, Signal Proc., August 1986, vol. ASSP-34(4), pp. 963–972 (1986)
Tekalp, M., Kaufman, H.: On Statistical Identification of a Class of Linear Space-Invariant Image Blurs Using Nonminimum-Phase ARMA Models. In: IEEE Trans. Acoustics, Speech, Signal Proc., August 1988, vol. 36(8), pp. 1360–1363 (1988)
Katsaggelos, K.: Maximum Likelihood Image Identification and Restoration Based on the EM Algorithm. In: Proc. 1989 Multidimensional Signal Processing Workshop (September 1989)
Biemond, J., van der Putten, F.G., Woods, J.: Identification and Restoration of Images with Symmetric Noncausal Blurs. IEEE Trans. Circuits, Systems 35(4), 385–393 (1988)
Subbarao, M., Wei, T.C., Surya, G.: Focused image recovery from two defocused images recorded with different camera settings. IEEE Transactions on Image Processing 4(12), 1613–1628 (1995)
Kubota, K., Kodama, K., Aizawa, K.: Registration and blur estimation method for multiple differently focused images. In: IEEE Proc. Int. Conf. Image Processing, vol. 2, pp. 515–519 (1999)
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© 2005 Springer-Verlag Berlin Heidelberg
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Shin, J., Maik, V., Lee, J., Paik, J. (2005). Multi-object Digital Auto-focusing Using Image Fusion. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_26
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DOI: https://doi.org/10.1007/11558484_26
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