RNLp : Mixing Non-Local and TV-Lp methods to remove impulse noise from images
Résumé
We propose a new variational framework to remove random-valued
impulse noise from images. This framework combines, in the same energy, a
non-local $L^p$ data term and a total variation regularization term. The
non-local $L^p$ term is a weighted $L^p$ distance between pixels, where
the weights depend on a robust distance between patches centered
at the pixels. In a first part, we study the theoretical properties of
the proposed energy, and we show how it is related to classical denoising models for extreme choices of the
parameters. In a second part, after having explained how to numerically find a
minimizer of the energy thanks to primal-dual approaches, we show
extensive denoising experiments on various images and noise
intensities. The denoising performances of the proposed methods are
on par with state of the art approaches, and the remarkable fact is that, unlike other
successful variational approaches for impulse noise removal, they do not rely on a noise detector.
Origine : Fichiers produits par l'(les) auteur(s)
Loading...