Set-Valued and Variational Analysis

, Volume 18, Issue 3, pp 277–306

A Second-Order Model for Image Denoising

Authors

    • UFR Sciences, Math., Labo. MAPMO, UMR 6628Université d’Orléans
  • Loic Piffet
    • UFR Sciences, Math., Labo. MAPMO, UMR 6628Université d’Orléans
Article

DOI: 10.1007/s11228-010-0156-6

Cite this article as:
Bergounioux, M. & Piffet, L. Set-Valued Anal (2010) 18: 277. doi:10.1007/s11228-010-0156-6

Abstract

We present a variational model for image denoising and/or texture identification. Noise and textures may be modelled as oscillating components of images. The model involves a L2-data fitting term and a Tychonov-like regularization term. We choose the BV2 norm instead of the classical BV norm. Here BV2 is the bounded hessian function space that we define and describe. The main improvement is that we do not observe staircasing effects any longer, during denoising process. Moreover, texture extraction can be performed with the same method. We give existence results and present a discretized problem. An algorithm close to the one set by Chambolle (J Math Imaging Vis 20:89–97, 2004) is used: we prove convergence and present numerical tests.

Keywords

Second order total variationImage reconstructionDenoisingTextureVariational method

Mathematics Subject Classifications (2010)

65D1868U1065K10
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© Springer Science+Business Media B.V. 2010