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Euler’s Approximations to Image Reconstruction

  • Dariusz Borkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)

Abstract

In this paper we present a new method to reconstruction of images with additive Gaussian noise. In order to solve this inverse problem we use stochastic differential equations with reflecting boundary (in short reflected SDEs). The continuous model of the image denoising is expressed in terms of such equations. The reconstruction algorithm is based on Euler’s approximations of solutions to reflected SDEs.

We consider a classical Euler scheme with random terminal time and controlled parameter of diffusion. The reconstruction time of our method is substantially reduced in comparison with classical Euler’s scheme. Our numerical experiments show that the new algorithm gives very good results and compares favourably with other image denoising filters.

Keywords

Wiener Process Noisy Image Image Denoising Reconstruction Time Terminal Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dariusz Borkowski
    • 1
  1. 1.Faculty of Mathematics and Computer ScienceNicolaus Copernicus UniversityToruńPoland

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