The face image super-resolution is a domain specific problem. Human face has complex, and fixed domain specific priors, which should be detail explored in super-resolution algorithm. This paper proposes an effective single image face super-resolution method by pre-clustering training data and Bayesian non-parametric learning. After pre-clustering, face patches from different clusters represent different areas in face, and also offer specific priors on these areas. Bayesian non-parametric learning captures consistent and accurate mapping between coupled spaces. Experimental results show that our method produces competitive results to other state-of-the-art methods, with much less computational time.
CITATION STYLE
Wu, J., Zhang, H., Xue, Y., Zhou, M., Xu, G., & Gao, Z. (2015). Single face image super-resolution via multi-dictionary bayesian non-parametric learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9489, pp. 540–548). Springer Verlag. https://doi.org/10.1007/978-3-319-26532-2_59
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