Numerical Methods and Applications in Total Variation Image Restoration

Raymond CHAN*, Tony F. CHAN, Andy YIP

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsBook ChapterResearchpeer-review

2 Citations (Scopus)


Since their introduction in a classic paper by Rudin, Osher, and Fatemi (Physica D 60:259–268, 1992), total variation minimizing models have become one of the most popular and successful methodologies for image restoration. New developments continue to expand the capability of the basic method in various aspects. Many faster numerical algorithms and more sophisticated applications have been proposed. This chapter reviews some of these recent developments.

Original languageEnglish
Title of host publicationHandbook of Mathematical Methods in Imaging
EditorsOtmar SCHERZER
PublisherSpringer New York
Number of pages37
ISBN (Electronic)9781493907908
ISBN (Print)9781493907892
Publication statusPublished - 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media New York 2011, 2015.


  • Augmented Lagrangian Method
  • Total Variation Minimization
  • Bregman Iteration
  • Total Variation Denoising
  • Split Bregman Iteration


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