An image magnification algorithm using the GVF constraint model

Xiaoguang Li, Kin Man Lam, Lansun Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

An image magnification method with a Gradient Vector Flow (GVF) constraint-based anisotropic diffusion model is proposed in this letter. A Low-Resolution (LR) image is first magnified using bilinear interpolation, and then an iterative image restoration method, with the use of an anisotropic diffusion model and a Gaussian moving-average constraint, is applied to the magnified image. The estimated GVF of a High-Resolution (HR) image can be used to remove the jagged effect and to preserve the textural structure in the image. Meanwhile, the use of the Gaussian moving-average LR model can provide a data fidelity constraint, which renders a magnified image closer to the ideal HR version. Experimental results show that the proposed method can improve the quality of magnified images in terms of both objective and subjective criteria.
Original languageEnglish
Pages (from-to)568-571
Number of pages4
JournalJournal of Electronics
Volume25
Issue number4
DOIs
Publication statusPublished - 1 Jan 2008

Keywords

  • Anisotropic diffusion
  • Gradient-Vector Flow (GVF)
  • Image magnification
  • Super resolution

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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