Texture Enhancing Based on Variational Image Decomposition

In this paper we consider the Augmented Lagrangian Method for image decomposition. We propose a method which decomposes an image into texture, which is characterized to have finite l(1) curvelet coefficients, a cartoon part, which has finite total variation norm, and noise and oscillating patterns, which have finite G-norm. In the second part of the paper we utilize the equivalence of the Augmented Lagrangian Method and the iterative Bregman distance regularization to show that the dual variables can be used for enhancing of particular components. We concentrate on the enhancing feature for the texture and propose two different variants of the Augmented Lagrangian Method for decomposition and enhancing.