Adaptive variational models for image decomposition

Image decomposition refers to the splitting of an image into two or more components. In this paper, a clean image is separated into two parts: one is the cartoon component, consisting only of geometric structure, and the other is the oscillatory component, consisting of texture. Three parts for noisy image are considered: cartoon, texture, and noise. To better decompose an image, we propose two new variational models. In our models, two adaptive regularization terms are introduced. The two regularization terms are determined by an adaptive function which can discriminate the cartoon and texture of an image automatically. Experimental results illustrate the effectiveness of the proposed models for image decomposition.