Noisy image decomposition: a new structure, texture and noise model based on local adaptivity
This addresses image processing for noisy data, but appears incremental as it builds on prior decomposition methods.
The paper tackles the problem of decomposing noisy images into structures, textures, and noise by proposing a new model based on local adaptivity, comparing it with existing work and introducing a combined model.
These last few years, image decomposition algorithms have been proposed to split an image into two parts: the structures and the textures. These algorithms are not adapted to the case of noisy images because the textures are corrupted by noise. In this paper, we propose a new model which decomposes an image into three parts (structures, textures and noise) based on a local regularization scheme. We compare our results with the recent work of Aujol and Chambolle. We finish by giving another model which combines the advantages of the two previous ones.