Abstract
This paper presents a colorization algorithm which produces color images from given monochrome images. Unlike previously proposed colorization methods, this paper formulates the colorization problem as the maximum a posteriori (MAP) estimation of a color image given a monochrome image. Markov random field (MRF) is used for modeling a color image which is utilized as a priori information for the MAP estimation. Under the mean field approximation, The MAP estimation problem for a whole image can be decomposed into local MAP estimation problems for each pixel. The local MAP estimation is described as a simple quadratic programming problem with constraints. Using 0.6% of whole pixels as references, the proposed method produced pretty high quality color images with 25.7 dB to 32.6 dB PSNR values for four standard images.
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References
Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. ACM Trans. Graphics 21, 277–280 (2002)
Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. ACM Trans. Graphics 23, 689–694 (2004)
Horiuchi, T.: Colorization algorithm using probabilistic relaxation. Image and Vision Computing 22, 197–202 (2004)
Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling using relaxation operation. IEEE Trans. Systems, Man and Cybernetics SMC-6, 420–433 (1976)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. & Machine Intell. PAMI-6, 721–741 (1984)
Spitzer, F.: Markov random fields and Gibbs ensembles. Amer. Math. Mon. 78, 142–154 (1971)
Huang, K.: Statistical mechanics, 2nd edn. John Wiley & Sons, Chichester (1987)
Noda, H., Shirazi, M.N., Kawaguchi, E.: MRF-based texture segmentation using wavelet decomposed images. Pattern Recognition 35, 771–782 (2002)
Mittelmann, H.D., Spellucci, P.: Decision tree for optimization software Web site, http://plato.asu.edu/guide.html
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© 2005 Springer-Verlag Berlin Heidelberg
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Noda, H., Korekuni, H., Takao, N., Niimi, M. (2005). Bayesian Colorization Using MRF Color Image Modeling. In: Ho, YS., Kim, HJ. (eds) Advances in Multimedia Information Processing - PCM 2005. PCM 2005. Lecture Notes in Computer Science, vol 3768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11582267_77
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DOI: https://doi.org/10.1007/11582267_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30040-3
Online ISBN: 978-3-540-32131-6
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