Learning-based super-resolution system using single facial image and multi-resolution wavelet synthesis

Shu Fan Lui, Jin Yi Wu, Hsi Shu Mao, Jenn Jier James Lien

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

A learning-based super-resolution system consisting of training and synthesis processes is presented. In the proposed system, a multi-resolution wavelet approach is applied to carry out the robust synthesis of both the global geometric structure and the local high-frequency detailed features of a facial image. In the training process, the input image is transformed into a series of images of increasingly lower resolution using the Haar discrete wavelet transform (DWT). The images at each resolution level are divided into patches, which are then projected onto an eigenspace to derive the corresponding projection weight vectors. In the synthesis process, a low-resolution input image is divided into patches, which are then projected onto the same eigenspace as that used in the training process. Modeling the resulting projection weight vectors as a Markov network, the maximum a posteriori (MAP) estimation approach is then applied to identity the best-matching patches with which to reconstruct the image at a higher level of resolution. The experimental results demonstrate that the proposed reconstruction system yields better results than the bi-cubic spline interpolation method.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Pages96-105
Number of pages10
EditionPART 2
ISBN (Print)9783540763895
DOIs
Publication statusPublished - 2007
Event8th Asian Conference on Computer Vision, ACCV 2007 - Tokyo, Japan
Duration: 2007 Nov 182007 Nov 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4844 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th Asian Conference on Computer Vision, ACCV 2007
Country/TerritoryJapan
CityTokyo
Period07-11-1807-11-22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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