Multiscale texture separation
This work addresses texture separation in image processing, which is incremental as it builds on existing models with theoretical improvements.
The paper tackles the problem of separating textures from images by proving a theorem that enables near-perfect extraction of certain textures using Meyer's decomposition model and a Littlewood-Paley filter, leading to a parameterless multiscale algorithm with extensions for directional separation, as demonstrated in experiments on synthetic and real images.
In this paper, we investigate theoretically the behavior of Meyer's image cartoon + texture decomposition model. Our main results is a new theorem which shows that, by combining the decomposition model and a well chosen Littlewood-Paley filter, it is possible to extract almost perfectly a certain class of textures. This theorem leads us to the construction of a parameterless multiscale texture separation algorithm. Finally, we propose to extend this algorithm into a directional multiscale texture separation algorithm by designing a directional Littlewood-Paley filter bank. Several experiments show the efficiency of the proposed method both on synthetic and real images.