A face hallucination algorithm via KPLS-eigentransformation model

Xiaoguang Li, Qing Xia, Li Zhuo, Kin Man Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Pages462-467
Number of pages6
DOIs
Publication statusPublished - 26 Nov 2012
Event2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012 - Hong Kong, Hong Kong
Duration: 12 Aug 201215 Aug 2012

Conference

Conference2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Country/TerritoryHong Kong
CityHong Kong
Period12/08/1215/08/12

Keywords

  • Eigentransformation
  • face halluciantion
  • Image super resolution
  • Kernel partial least squares

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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