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Reports Year : 2003

Image Denoising using Stochastic Differential Equations

Xavier Descombes
Elena Zhizhina
  • Function : Author

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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Dates and versions

inria-00071772 , version 1 (23-05-2006)

Identifiers

  • HAL Id : inria-00071772 , version 1

Cite

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