Details

Autor(en) / Beteiligte

• Aujol, Jean-Fran ois
• Chambolle, Antonin

Titel

Dual Norms and Image Decomposition Models

Ist Teil von

• International journal of computer vision, 2005-06, Vol.63 (1), p.85-104

Ort / Verlag

New York: Springer Nature B.V

Erscheinungsjahr

2005

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Following a recent work by Y. Meyer, decomposition models into a geometrical component and a textured component have recently been proposed in image processing. In such approaches, negative Sobolev norms have seemed to be useful to modelize oscillating patterns. In this paper, we compare the properties of various norms that are dual of Sobolev or Besov norms. We then propose a decomposition model which splits an image into three components: a first one containing the structure of the image, a second one the texture of the image, and a third one the noise. Our decomposition model relies on the use of three different semi-norms: the total variation for the geometrical component, a negative Sobolev norm for the texture, and a negative Besov norm for the noise. We illustrate our study with numerical examples.[PUBLICATION ABSTRACT]