[IPOL announce] new article: Chambolle's Projection Algorithm for Total Variation Denoising

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2013/61/

Chambolle's Projection Algorithm for Total Variation Denoising
by Joan Duran, Bartomeu Coll, Catalina Sbert
Image Processing On Line, vol. 2013, pp. 301–321.
http://www.ipol.im/pub/art/2013/61/

Abstract
Denoising is the problem of removing the inherent noise from an image. 
The standard noise model is additive white Gaussian noise, where the 
observed image f is related to the underlying true image u by the 
degradation model f=u+n, and n is supposed to be at each pixel 
independently and identically distributed as a zero-mean Gaussian random 
variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi 
introduced the total variation as a regularizing term. It has proved to 
be quite efficient for regularizing images without smoothing the 
boundaries of the objects.

This paper focuses on the simple description of the theory and on the 
implementation of Chambolle's projection algorithm for minimizing the 
total variation of a grayscale image. Furthermore, we adapt the 
algorithm to the vectorial total variation for color images. The 
implementation is described in detail and its parameters are analyzed 
and varied to come up with a reliable implementation.