IPOL Journal · Image Processing On Line
- published
- 2012-05-19
- reference
- Pascal Getreuer, Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman, Image Processing On Line, 2 (2012), pp. 74–95. https://doi.org/10.5201/ipol.2012.g-tvd
Communicated by Jean-Michel Morel
Demo edited by Pascal Getreuer
Abstract
Denoising is the problem of removing noise from an image. The most commonly studied case is with additive white Gaussian noise (AWGN), where the observed noisy image f is related to the underlying true image u by f=u+η and η is at each point in space independently and identically distributed as a zero-mean Gaussian random variable.
Total variation (TV) regularization is a technique that was originally developed for AWGN image denoising by Rudin, Osher, and Fatemi. The TV regularization technique has since been applied to a multitude of other imaging problems, see for example Chan and Shen's book. We focus here on the split Bregman algorithm of Goldstein and Osher for TV-regularized denoising.
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- full text manuscript: PDF (1.1M)
- source code: TAR/GZ
History
- the current version was published on 2012-12-04, with a typographic error corrected in math formula 35 page 8
- the article was converted to PDF on 2012-08-07, with two typographic errors corrected in math formulas: manuscript, source code
- the original version was published on 2012-05-19: manuscript, source code
- Note from the editor: the manuscript of the article was modified on 2022-01-01 to include information about its editors. The original version of the manuscript is available here.
