Image Decompositions Using Spaces of Variable Smoothness and Integrability
Juha Tiirola · SIAM Journal on Imaging Sciences · 2014
In this paper new variational image decomposition models are proposed which split an image into its geometrical component and oscillating component. An interpolation of total variation regularization and isotropic smoothing is used as the structure energy, while the energy for the oscillating component is a modular in a variable index Besov space. The existence of minimizers of the new structure-texture energies is studied. The proper choices of the bounded variation--Sobolev integrability and the Besov indices aim to reduce oversmoothing of the cartoon component. If there is noise present, the choices help reduce staircasing. Experimental results demonstrate the possible advantages of the variable index models over the constant index models.