Difference between revisions of "Publications/darbon.05.tr"
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{{Publication |
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| date = 2005-01-01 |
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| authors = Jérôme Darbon, Marc Sigelle |
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| number = 2005D002 |
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| address = Paris, France |
| address = Paris, France |
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| annote = This technical report corresponds to the publication darbon.05.ibpria. |
| annote = This technical report corresponds to the publication darbon.05.ibpria. |
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⚫ | | abstract = This paper deals with the minimization of the total variation under a convex data fidelity term. We propose an algorithm which computes an exact minimizer of this problem. The method relies on the decomposition of an image into its level sets. Using these level sets, we map the problem into optimizations of independent binary Markov Random Fields. Binary solutions are found thanks to graph-cut techniques and we show how to derive a fast algorithm. We also study the special case when the fidelity term is the <math>L^1</math>-norm. Finally we provide some experiments. |
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⚫ | | abstract = This paper deals with the minimization of the total variation under a convex data fidelity term. We propose an algorithm which computes an exact minimizer of this problem. The method relies on the decomposition of an image into its level sets. Using these level sets, we map the problem into optimizations of independent binary Markov Random Fields. Binary solutions are found thanks to graph-cut techniques and we show how to derive a fast algorithm. We also study the special case when the fidelity term is the |
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address = <nowiki>{</nowiki>Paris, France<nowiki>}</nowiki>, |
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annote = <nowiki>{</nowiki>This technical report corresponds to the publication |
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darbon.05.ibpria.<nowiki>}</nowiki>, |
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Latest revision as of 18:19, 5 January 2018
- Authors
- Jérôme Darbon, Marc Sigelle
- Place
- Paris, France
- Type
- techreport
- Projects
- Olena
- Keywords
- Image
- Date
- 2005-01-01
Abstract
This paper deals with the minimization of the total variation under a convex data fidelity term. We propose an algorithm which computes an exact minimizer of this problem. The method relies on the decomposition of an image into its level sets. Using these level sets, we map the problem into optimizations of independent binary Markov Random Fields. Binary solutions are found thanks to graph-cut techniques and we show how to derive a fast algorithm. We also study the special case when the fidelity term is the -norm. Finally we provide some experiments.
Bibtex (lrde.bib)
@TechReport{ darbon.05.tr, author = {J\'er\^ome Darbon and Marc Sigelle}, title = {A fast and exact algorithm for total variation minimization}, institution = {ENST}, year = 2005, number = {2005D002}, address = {Paris, France}, month = jan, annote = {This technical report corresponds to the publication darbon.05.ibpria.}, abstract = {This paper deals with the minimization of the total variation under a convex data fidelity term. We propose an algorithm which computes an exact minimizer of this problem. The method relies on the decomposition of an image into its level sets. Using these level sets, we map the problem into optimizations of independent binary Markov Random Fields. Binary solutions are found thanks to graph-cut techniques and we show how to derive a fast algorithm. We also study the special case when the fidelity term is the $L^1$-norm. Finally we provide some experiments.} }