Image denoising using mean curvature of image surface

Wei Zhu*, Tony Chan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

109 Scopus citations

Abstract

We propose a new variational model for image denoising, which employs the L1norm of the mean curvature of the image surface (x, f(x)) of a given image f : Ω → ℝ. Besides eliminating noise and preserving edges of objects efficiently, our model can keep corners of objects and greyscale intensity contrasts of images and also remove the staircase effect. In this paper, we analytically study the proposed model and justify why our model can preserve object corners and image contrasts. We apply the proposed model to the denoising of curves and plane images, and also compare the results with those obtained by using the classical Rudin-Osher-Fatemi model [Phys. D, 60 (1992), pp. 259- 268].

Original languageEnglish (US)
Pages (from-to)1-32
Number of pages32
JournalSIAM Journal on Imaging Sciences
Volume5
Issue number1
DOIs
StatePublished - Jan 1 2012

Keywords

  • Image denoising
  • Mean curvature
  • Variational model

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

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