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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Noda, H., Korekuni, H., Takao, N., & Niimi, M. (2005). Bayesian colorization using MRF color image modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3768 LNCS, pp. 889–899). https://doi.org/10.1007/11582267_77
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