Abstract
Image restoration is the inverse process of image degradation. Based on the general imaging model to represent image degradation, various restoration algorithms have been designed. However, none of these algorithms have taken into account the inherent properties of Homology-Continuity in the image degradation process. Such neglect leads to the ill-posedness and detail loss that can not be completely overcome by the traditional image restoration. In this paper, according to the Principle of Homology-Continuity (PHC) proposed in High Dimensional Biomimetic Informatics, we will offer insight into image degradation process and discuss the advantages of the corresponding restoration algorithm.
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References
Banham, M.R., Katsaggelos, A.K.: Digital image restoration. IEEE Signal Processing Magazine, 24–41 (1997)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2002)
Wang, S.: Biomimetic(Topological) Pattern Recognition — A New Model of Pattern Recognition Theory and Its Applications. Chinese Journal of Electronics 30(10), 1417–1420 (2002)
Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-Posed Problems. Wiley, New York (1977)
Katsaggelos, A.K., Biemond, J., Schafer, R.W., Mersereau, R.M.: A Regularized Iterative Image Restoration Algorithm. IEEE Trans. Acoust., Speech, Signal Proc. 39, 914–929 (1991)
Reeves, S.J., Mersereau, R.M.: Blur identification by the method of generalized cross-validation. IEEE Trans. Imuge Processing 1(3), 301–311 (1992)
Lagendijk, R.L., Biemond, J., Boekee, D.E.: Identification and restoration of noisy blurred images using the expectation-maximization algorithm. IEEE Trans. Acoust, Speech, Signal Processing 38(7) (July 1990)
Ayers, G.R., Dainty, J.C.: Iterative blind deconvolution method and its applications. Optics Letters 13(7), 547–549 (1988)
Wang, S.: First Step to Multi-Demensional Space Biomimetic Informatics. National Defence Industry Press, Beijing (2008)
Andrews, H.C., Hunt, B.R.: Digital Image Restoration. Prentice-Hall, Inc., New Jersey (1977)
Hansen, P.C., Nagy, J.G., O’Leary, D.P.: Deblurring Images: Matrices, Spectra, and Filtering, Fundam, vol. 3. SIAM, Philadelphia (2006)
Yu, C.: Study on Image Debluring Based on High-Dimensional Biomimetic Informatics. In: CAS. Institute of Semiconductor, Beijing (2007)
Yang, C.: Research on High-Dimensional Biomimetic Information Processing and Its Applications. In: CAS. Institute of Semiconductor, Beijing (2010)
Kundur, D., Hatzinakos, D.: Blind Image Deconvolution. IEEE Signal Processing Magazine 13(3), 43–64 (1996)
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Chen, L., Li, W., Chen, C. (2011). Research on the Principle of Homology-Continuity in Image Degradation. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_32
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DOI: https://doi.org/10.1007/978-3-642-23887-1_32
Publisher Name: Springer, Berlin, Heidelberg
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