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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.
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Submitted on : Tuesday, May 23, 2006 - 6:43:58 PM
Last modification on : Monday, October 12, 2020 - 10:30:17 AM
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  • HAL Id : inria-00071772, version 1



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



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