Example-based image super-resolution with class-specific predictors

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

50 Citations (Scopus)

Abstract

Example-based super-resolution is a promising approach to solving the image super-resolution problem. However, the learning process can be slow and prediction can be inaccurate. In this paper, we present a novel learning-based algorithm for image super-resolution to improve the computational speed and prediction accuracy. Our new method classifies image patches into several classes, for each class, a class-specific predictor is designed. A class-specific predictor takes a low-resolution image patch as input and predicts a corresponding high-resolution patch as output. The performances of the class-specific predictors are evaluated using different datasets formed by face images and natural-scene images. We present experimental results which demonstrate that the new method provides improved performances over existing methods.
Original languageEnglish
Pages (from-to)312-322
Number of pages11
JournalJournal of Visual Communication and Image Representation
Volume20
Issue number5
DOIs
Publication statusPublished - 1 Jul 2009

Keywords

  • Class-specific predictor
  • Content-based encoding
  • Domain-specific training set
  • Example-based super-resolution
  • General-purpose training set
  • Human face magnification
  • Self-specific training set
  • Vector quantization

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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