Numerical Methods and Applications in Total Variation Image Restoration

Raymond CHAN; Tony F. CHAN; Andy YIP

doi:10.1007/978-1-4939-0790-8_24

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 Proceedings › Book Chapter › Research › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Handbook of Mathematical Methods in Imaging
Editors	Otmar SCHERZER
Publisher	Springer New York
Pages	1501-1537
Number of pages	37
ISBN (Electronic)	9781493907908
ISBN (Print)	9781493907892
DOIs	https://doi.org/10.1007/978-1-4939-0790-8_24
Publication status	Published - 2015
Externally published	Yes

Bibliographical note

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

Keywords

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

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10.1007/978-1-4939-0790-8_24

Cite this

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KW - Augmented Lagrangian Method

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KW - Bregman Iteration

KW - Total Variation Denoising

KW - Split Bregman Iteration

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Numerical Methods and Applications in Total Variation Image Restoration

Abstract

Bibliographical note

Keywords

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