Adaptive variational models for image decomposition

An Adaptive Variational Model for Image Decomposition

We propose a new model for image decomposition which separates an image into a cartoon, consisting only of geometric objects, and an oscillatory component, consisting of textures and noise. The model is given in a variational formulation with adaptive regularization norms for both the cartoon and texture part. The energy for the cartoon interpolates between total variation regularization and is...

Variational domain decomposition for parallel image processing

A class of quasi-variational inequalities for adaptive image denoising and decomposition

We introduce a class of adaptive non-smooth convex variational problems for image denoising in terms of a common data fitting term and a support functional as regularizer. Adaptivity is modeled by a set-valued mapping with closed, compact and convex values, that defines and steers the regularizer depending on the variational solution. This extension gives rise to a class of quasi-variational in...

Variational structure-texture image decomposition on manifolds

This paper considers the problem of decomposing an image defined on a manifold into a structural component and a textural component. We formulate such decomposition as a variational problem, in which the total variation energy is used for extracting the structural part and based on the properties of texture one of three norms, L, L and G, is used in the fidelity term for the textural part. Whil...

Image Variational Decomposition Based on Dual Method

In the paper, we firstly recommend a new variational model for image decomposition into cartoon and texture or noise by introducing a new function in Sobolev space, in order to overcome the inconsistency between the theoretical model and numerical simulation. Secondly, we prove the existence of minimal solutions of the improved ROF energy functional. Subsequently, we also introduce two addition...