Authors:
Jérôme Fehrenbach
1
;
Pierre Weiss
2
and
Corinne Lorenzo
3
Affiliations:
1
IMT, France
;
2
Toulouse University, France
;
3
ITAV, France
Keyword(s):
Denoising, Cartoon+texture decomposition, Primal-dual algorithm, Stationary noise, Fluorescence microscopy.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Bayesian Models
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Convex Optimization
;
Health Engineering and Technology Applications
;
Medical Imaging
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Sparsity
;
Theory and Methods
Abstract:
Starting with a book of Y.Meyer in 2001, negative norm models attracted the attention of the imaging community in the last decade. Despite numerous works, these norms seem to have provided only luckwarm results in practical applications. In this work, we propose a framework and an algorithm to remove stationary noise from images. This algorithm has numerous practical applications and we show it on 3D data from a newborn microscope called SPIM. We also show that this model generalizes Meyer’s model and its successors in the discrete setting and allows to interpret them in a Bayesian framework. It sheds a new light on these models and allows to pick them according to some a priori knowledge on the texture statistics. Further results are available on our webpage at http://www.math.univ-toulouse.fr/~weiss/PagePublications.html.