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Title: A face hallucination algorithm via KPLS-eigentransformation model
Authors: Li, X
Xia, Q
Zhuo, L
Lam, KM 
Keywords: Eigentransformation
Image super resolution
Kernel partial least squares
Face halluciantion
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC), 12-15 August 2012, Hong Kong, p. 462-467 How to cite?
Abstract: In this paper, we present a novel eigentransformation based algorithm for face hallucination. The traditional eigentransformation method is a linear subspace approach, which represents an image as a linear combination of training samples. Consequently, it cannot effectively represent the relationship between the low resolution facial images and the corresponding high-resolution version. In our algorithm, a Kernel Partial Least Squares (KPLS) predictor is introduced into the eigentransformation model for solving the High Resolution (HR) image form a Low Resolution (LR) facial image. We have compared our proposed method with some current Super Resolution (SR) algorithms using different zooming factors. Experimental results show that our algorithm provides improved performances over the compared methods in terms of both visual quality and numerical errors.
ISBN: 978-1-4673-2192-1
DOI: 10.1109/ICSPCC.2012.6335599
Appears in Collections:Conference Paper

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