Variational Decomposition Model in Besov Spaces and Negative Hilbert-Sobolev Spaces

In this paper, we propose a new variational decomposition model which splits an image into two components: a first one containing the structure and a second one the texture or noise. Our decomposition model relies on the use of two semi-norms: the Besov semi-norm for the geometrical component, the negative Hilbert-Sobolev norms for the texture or noise. And the proposed model can be understood as generalizations of Daubechies-Teschke's model and have been motivated also by Lorenz's idea. And we illustrate our study with numerical examples for image decomposition and denoising.

• Type: Conference
• Year: 2006
• Where: CIS
• Authors: Min Li, Xiangchu Feng
• Keywords: Applied Computing, CIS 2006, Decomposition Model, Negative Hilbert-sobolev Norms, Variational Decomposition Model