# Efficient schemes for total variation minimization under constraints in image processing

1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : This paper presents new algorithms to minimize total variation and more generally $l^1$-norms under a general convex constraint. The algorithms are based on a recent advance in convex optimization proposed by Yurii Nesterov. Depending on the regularity of the data fidelity term, we solve either a primal problem, either a dual problem. First we show that standard first order schemes allow to get solutions of precision $\epsilon$ in $O(\frac{1}{\epsilon^2})$ iterations at worst. For a general convex constraint, we propose a scheme that allows to obtain a solution of precision $\epsilon$ in $O(\frac{1}{\epsilon})$ iterations. For a strongly convex constraint, we solve a dual problem with a scheme that requires $O(\frac{1}{\sqrt{\epsilon}})$ iterations to get a solution of precision $\epsilon$. Thus, depending on the regularity of the data term, we gain from one to two orders of magnitude in the convergence rates with respect to standard schemes. Finally we perform some numerical experiments which confirm the theoretical results on various problems.
Keywords :
Document type :
Reports
Domain :

Cited literature [48 references]

https://hal.inria.fr/inria-00166096
Contributor : Pierre Weiss Connect in order to contact the contributor
Submitted on : Friday, March 7, 2008 - 5:40:30 PM
Last modification on : Wednesday, September 15, 2021 - 12:06:02 PM
Long-term archiving on: : Friday, November 25, 2016 - 9:36:56 PM

### Files

RR-6260.pdf
Files produced by the author(s)

### Identifiers

• HAL Id : inria-00166096, version 3

### Citation

Pierre Weiss, Gilles Aubert, Laure Blanc-Féraud. Efficient schemes for total variation minimization under constraints in image processing. [Research Report] RR-6260, INRIA. 2007, pp.36. ⟨inria-00166096v3⟩

Record views