[IPOL announce] new article: Chambolle's Projection Algorithm for Total Variation Denoising
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Wed Dec 18 10:44:30 CET 2013
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.
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