Numerical Methods and Applications in Total Variation Image Restoration

Living reference work entry

Abstract

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.

Keywords

Total Variation Minimization Bregman Iteration Split Bregman Method Total Variation Denoising Split Bregman Iteration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of Mathematics, The Chinese University of Hong KongShatinHong Kong
  2. 2.Office of the President, Hong Kong University of Science and TechnologyClear Water BayHong Kong
  3. 3.Department of Mathematics, Hong Kong Baptist UniversityKowloon Tong Hong Kong

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