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Journal of Electronic Imaging

Image colorization using Bayesian nonlocal inference
Author(s): Chen Yao; Xiaokang Yang; Li Chen; Yi Xu
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Paper Abstract

Colorization is the process of adding colors to monochrome images. State-of-the-art colorization methods can be generally categorized into example-based colorization and scribble-based algorithms. In this paper, we present a new scribble-based colorization algorithm based on Bayesian inference and nonlocal likelihood computation. We convert the process of image colorization to a probability optimization problem in this Bayesian framework, where we use nonlocal-mean likelihood computation and Markov random field prior's. The expectation maximization method is used to solve an optimization object function. Finally, experimental results demonstrate the effectiveness of the proposed algorithm

Paper Details

Date Published: 1 April 2011
PDF: 7 pages
J. Electron. Imaging. 20(2) 023008 doi: 10.1117/1.3582139
Published in: Journal of Electronic Imaging Volume 20, Issue 2
Show Author Affiliations
Chen Yao, Shanghai Jiao Tong Univ. (China)
Xiaokang Yang, Shanghai Jiao Tong Univ. (China)
Li Chen, Shanghai Jiao Tong Univ. (China)
Yi Xu, Shanghai Jiao Tong Univ. (China)

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