A learning approach for single-frame face super-resolution

Yu He, Kim Hui Yap, Lap Pui Chau

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

5 Citations (Scopus)

Abstract

This paper presents a new learning approach for single-frame face super-resolution (SR). The aim of face SR is to estimate the missing high-resolution (HR) information from a single low-resolution (LR) face image by learning from training samples in the database. A commonly encountered issue in conventional face SR methods is that when the given LR image is a new face significantly different from those in the database, the quality of the reconstructed HR face is usually unsatisfactory. To alleviate this difficulty, we develop a new method to perform face SR based on principal component analysis (PCA) and locally linear embedding (LLE). The reconstructed HR face is able to preserve standard facial features and detailed local information through a residue prediction method using manifold learning. Experimental results show that the proposed method is effective in performing single-frame face SR.

Original languageEnglish
Title of host publication2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Pages770-773
Number of pages4
DOIs
Publication statusPublished - May 2009
Externally publishedYes
Event2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009 - Taipei, Taiwan
Duration: 24 May 200927 May 2009

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Conference

Conference2009 IEEE International Symposium on Circuits and Systems, ISCAS 2009
Country/TerritoryTaiwan
CityTaipei
Period24/05/0927/05/09

Keywords

  • Face image super-resolution
  • Locally linear embedding
  • Principal component analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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