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Image colorization using Bayesian nonlocal inference

[+] Author Affiliations
Chen Yao

Shanghai Jiao Tong University, Institute of Image Communication and Information Processing Shanghai 200240, China

Xiaokang Yang

Shanghai Jiao Tong University, Institute of Image Communication and Information Processing Shanghai 200240, China

Li Chen

Shanghai Jiao Tong University, Institute of Image Communication and Information Processing Shanghai 200240, China

Yi Xu

Shanghai Jiao Tong University, Institute of Image Communication and Information Processing Shanghai 200240, China

J. Electron. Imaging. 20(2), 023008 (May 06, 2011). doi:10.1117/1.3582139
History: Received March 16, 2011; Accepted April 01, 2011; Revised February 19, 2011; Published May 06, 2011; Online May 06, 2011
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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.

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Citation

Chen Yao ; Xiaokang Yang ; Li Chen and Yi Xu
"Image colorization using Bayesian nonlocal inference", J. Electron. Imaging. 20(2), 023008 (May 06, 2011). ; http://dx.doi.org/10.1117/1.3582139


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