HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Reports

Image Denoising using Stochastic Differential Equations

Xavier Descombes 1 Elena Zhizhina
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : We address the problem of image denoising using a Stochastic Differential Equation approach. We consider a diffusion process which converges to a Gibbs measure. The Hamiltonian of the Gibbs measure embeds an interaction term, providing smoothing properties, and a data term. We study two discrete approximations of the Langevin dynamics associated with this diffusion process: the Euler and the Explicit Strong Taylor approximations. We compare the convergence speed of the associated algorithms and the Metropolis-Hasting algorithm. Results are shown on synthetic and real data. They show that the proposed approach provides better results when considering a small number of iterations.
Document type :
Reports
Complete list of metadata

Cited literature [1 references]  Display  Hide  Download

https://hal.inria.fr/inria-00071772
Contributor : Rapport de Recherche Inria Connect in order to contact the contributor
Submitted on : Tuesday, May 23, 2006 - 6:43:58 PM
Last modification on : Friday, February 4, 2022 - 3:18:06 AM
Long-term archiving on: : Sunday, April 4, 2010 - 10:37:14 PM

Identifiers

  • HAL Id : inria-00071772, version 1

Collections

Citation

Xavier Descombes, Elena Zhizhina. Image Denoising using Stochastic Differential Equations. RR-4814, INRIA. 2003. ⟨inria-00071772⟩

Share

Metrics

Record views

2492

Files downloads

19978