Face hallucination based on nonparametric Bayesian learning

Minqi Li, Richard Yi Da Xu, Xiangjian He

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages986-990
Number of pages5
ISBN (Electronic)9781479983391
DOIs
Publication statusPublished - 9 Dec 2015
Externally publishedYes
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period27/09/1530/09/15

Keywords

  • ddCRP
  • Face hallucination
  • nonparametric Bayesian

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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