Journal
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
Volume 74, Issue 1, Pages 59-67Publisher
SPRINGER
DOI: 10.1007/s11265-013-0809-4
Keywords
Colorization; MAP; MRF; LLC
Funding
- National Natural Science Foundation of China [61170142]
- National Key Technology RD Program [2011BAG05B04]
- International Science & Technology Cooperation Program of China [2013DFG12840]
- Fundamental Research Funds for the Central Universities
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Colorization for gray scale facial image is an important technique in various practical applications. However, the methods that have been proposed are essentially semi-automatic. In this paper, we present a new probabilistic framework based on Maximum A Posteriori (MAP) estimation to automatically transform the given gray scale facial image to corresponding color one. Firstly, the input image is divided into several patches and non-parametric Markov random field (MRF) is employed to formulate the global energy. Secondly, Locality-constrained Linear Coding (LLC) is employed to learn the color distribution for each patch. At the same time, the simulated annealing algorithm is employed to iteratively update the patches chosen by LLC to optimize the MRF by decreasing global energy cost. The experimental results demonstrate that the proposed framework is effective to colorize the gray scale facial images to corresponding color ones.
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