1 April 2011 Image colorization using Bayesian nonlocal inference
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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
© (2011) Society of Photo-Optical Instrumentation Engineers (SPIE)
Chen Yao, Chen Yao, Xiaokang Yang, Xiaokang Yang, Li Chen, Li Chen, Yi Xu, Yi Xu, } "Image colorization using Bayesian nonlocal inference," Journal of Electronic Imaging 20(2), 023008 (1 April 2011). https://doi.org/10.1117/1.3582139 . Submission:


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