Authors:
S. Jehan-Besson
1
and
Jonas Koko
2
Affiliations:
1
CNRS, France
;
2
Clermont Université, Université Blaise Pascal;CNRS, France
Keyword(s):
Total variation, L1 norm, Augmented Lagrangian, Fenchel duality, Uzawa methods, Salt and pepper noise removal.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Enhancement and Restoration
;
Image Filtering
;
Image Formation and Preprocessing
Abstract:
In this paper, the minimization of a weighted total variation regularization term (denoted TVg) with L1 norm as the data fidelity term is addressed using the Uzawa block relaxation method. Numerical experiments show the availability of our algorithm for salt and pepper noise removal and its robustness against the choice of the penalty parameter. This last property is useful to attain the convergence in a reduced number of iterations leading to efficient numerical schemes. The specific role of the function g in the weighted total variation term is also investigated and we show that an appropriate choice leads to a significant improvement of the final denoising results. Using this function, we propose a whole algorithm for salt and pepper noise removal (UBR-EDGE) that is able to handle high noise levels at a low computational cost.