Augmented Lagrangian method for a mean curvature based image denoising model

Wei Zhu, Xue Cheng Tai, Tony Chan

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

High order derivative information has been widely used in developing variational models in image processing to accomplish more advanced tasks. However, it is a nontrivial issue to construct efficient numerical algorithms to deal with the minimization of these variational models due to the associated high order Euler-Lagrange equations. In this paper, we propose an efficient numerical method for a mean curvature based image denoising model using the augmented Lagrangian method. A special technique is introduced to handle the mean curvature model for the augmented Lagrangian scheme. We detail the procedures of finding the related saddle-points of the functional. We present numerical experiments to illustrate the effectiveness and efficiency of the proposed numerical method, and show a few important features of the image denoising model such as keeping corners and image contrast. Moreover, a comparison with the gradient descent method further demonstrates the efficiency of the proposed augmented Lagrangian method.

Original languageEnglish (US)
Pages (from-to)1409-1432
Number of pages24
JournalInverse Problems and Imaging
Volume7
Issue number4
DOIs
StatePublished - Nov 1 2013

Keywords

  • Augmented Lagrangian method
  • Image denoising
  • Mean curvature

ASJC Scopus subject areas

  • Analysis
  • Modeling and Simulation
  • Discrete Mathematics and Combinatorics
  • Control and Optimization

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