An efficient example-based approach for image super-resolution

Xiaoguang Li, Kin Man Lam, Guoping Qiu, Lansun Shen, Suyu Wang

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

14 Citations (Scopus)

Abstract

A novel algorithm for image super-resolution with class-specific predictors is proposed in this paper. In our algorithm, the training example images are classified into several classes, and each patch of a low-resolution image is classified into one of these classes. Each class has its high-frequency information inferred using a class-specific predictor, which is trained via the training samples from the same class. In this paper, two different types of training sets are employed to investigate the impact of the training database to be used. Experimental results have shown the superior performance of our method.
Original languageEnglish
Title of host publication2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP
Pages575-580
Number of pages6
DOIs
Publication statusPublished - 22 Sep 2008
Event2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP - Zhenjiang, China
Duration: 7 Jun 200811 Jun 2008

Conference

Conference2008 IEEE International Conference Neural Networks and Signal Processing, ICNNSP
Country/TerritoryChina
CityZhenjiang
Period7/06/0811/06/08

Keywords

  • Class-specific predictor
  • Example-based Super-resolution
  • Human face magnification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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