Title
Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Abstract
This paper deals with the total variation minimization problem in image restoration for convex data fidelity functionals. We propose a new and fast algorithm which computes an exact solution in the discrete framework. Our method relies on the decomposition of an image into its level sets. It maps the original problems into independent binary Markov Random Field optimization problems at each level. Exact solutions of these binary problems are found thanks to minimum cost cut techniques in graphs. These binary solutions are proved to be monotone increasing with levels and yield thus an exact solution of the discrete original problem. Furthermore we show that minimization of total variation under L 1 data fidelity term yields a self-dual contrast invariant filter. Finally we present some results.
Year
DOI
Venue
2006
10.1007/s10851-006-8803-0
Journal of Mathematical Imaging and Vision
Keywords
Field
DocType
restoration,total variation,level sets,Markov random fields,graph cuts
Cut,Mathematical optimization,Markov random field,Level set,Invariant (mathematics),Image restoration,Optimization problem,Monotone polygon,Mathematics,Binary number
Journal
Volume
Issue
ISSN
26
3
0924-9907
Citations 
PageRank 
References 
90
4.89
27
Authors
2
Name
Order
Citations
PageRank
Jérôme Darbon151241.96
Marc Sigelle231634.12