Incorporating known features into a total variation dictionary model for source separation

The goal of this paper is to investigate the impact of dictionary choosing for a total variation dictionary model. After theoretical analysis, we present the experiments in which the dictionary contains the curvatures of known forms (letters). The data-fidelity term of this model allows the appearance in the residue of all structures except forms being used to build the dictionary. Therefore, these forms will remain in the result image while the other structures will disappear. Our experiments are carried on the source separation problem and confirm this impression. The starting image contains letters (known) on a very structured background (an image). We show that it is possible, with this model, to obtain a reasonable separation of these structures. Finally, this work illustrates clearly that the dictionary must contain the curvature of elements which we seek to preserve.