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research
Face hallucination based on nonparametric Bayesian learning
Authors
RY Da Xu
X He
M Li
Publication date
9 December 2015
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
© 2015 IEEE. In this paper, we propose a novel example-based face hallucination method through nonparametric Bayesian learning based on the assumption that human faces have similar local pixel structure. We cluster the low resolution (LR) face image patches by nonparametric method distance dependent Chinese Restaurant process (ddCRP) and calculate the centres of the clusters (i.e., subspaces). Then, we learn the mapping coefficients from the LR patches to high resolution (HR) patches in each subspace. Finally, the HR patches of input low resolution face image can be efficiently generated by a simple linear regression. The spatial distance constraint is employed to aid the learning of subspace centers so that every subspace will better reflect the detailed information of image patches. Experimental results show our method is efficient and promising for face hallucination
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info:doi/10.1109%2Ficip.2015.7...
Last time updated on 03/08/2021
OPUS - University of Technology Sydney
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oai:opus.lib.uts.edu.au:10453/...
Last time updated on 13/02/2017