Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman
Pascal Getreuer
→ BibTeX
    title   = {{Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman}},
    author  = {Getreuer, Pascal},
    journal = {{Image Processing On Line}},
    volume  = {2},
    pages   = {74--95},
    year    = {2012},
    doi     = {10.5201/ipol.2012.g-tvd},
% if your bibliography style doesn't support doi fields:
    note    = {\url{}}
Pascal Getreuer, Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman, Image Processing On Line, 2 (2012), pp. 74–95.

Communicated by Jean-Michel Morel
Demo edited by Pascal Getreuer


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.