Bayesian colorization using MRF color image modeling

Authors:
Hideki Noda;Hitoshi Korekuni;Nobuteru Takao;Michiharu Niimi
Affiliations:
Dept. of Systems Innovation and Informatics, Kyushu Institute of Technology, Iizuka, Japan;Dept. of Electrical, Electronic and Computer Engineering, Kyushu Institute of Technology, Kitakyushu, Japan;ASA Systems Inc., Kitakyushu, Japan;Dept. of Systems Innovation and Informatics, Kyushu Institute of Technology, Iizuka, Japan
Venue:
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Year:
2005

Citing 2
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Transferring color to greyscale images

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Colorization using optimization

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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.