A novel example-based super-resolution approach based on patch classification and the KPCA prior model

Yu Hu, Kin Man Lam, Tingzhi Shen, Sanyuan Zhao

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

3 Citations (Scopus)

Abstract

In this paper, we propose a novel example-based super-resolution method to hallucinate high-resolution images from low-resolution images. As example-based super-resolution is a kind of learning process, how to learn effectively from training samples is essential to the quality of the reconstructed images. In our algorithm, a classification process is firstly employed to construct a well-organized patch database. Then, the KPCA prior model is used for each class to infer the high-resolution output. Since the training samples or patches are divided into numerous classes, the variations among the patches in each class or cluster are therefore greatly reduced. In addition, KPCA can capture the high-order statistics in those training samples, which makes the learning process even more powerful. Experiments show that the proposed algorithm can provide a high quality for image superresolution reconstruction.
Original languageEnglish
Title of host publicationProceedings - 2008 International Conference on Computational Intelligence and Security, CIS 2008
Pages6-11
Number of pages6
Volume1
DOIs
Publication statusPublished - 1 Dec 2008
Event2008 International Conference on Computational Intelligence and Security, CIS 2008 - Suzhou, China
Duration: 13 Dec 200817 Dec 2008

Conference

Conference2008 International Conference on Computational Intelligence and Security, CIS 2008
Country/TerritoryChina
CitySuzhou
Period13/12/0817/12/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

Cite this