Abstract: A novel method is presented for separating images into texture and cartoon parts based on sparse representations and a projected regularization scheme. The basic idea presented in this paper is the use of two appropriate dictionaries, one for the representation of texture parts-the dual tree complex wavelet transform and the other for the cartoon parts-the second generation of curvelet transform followed by a projected regularization method which is employed to better direct the separation process and reduce the pseudo-Gibbs oscillations. Both dictionaries are chosen such that they lead to sparse representations over one type of image-content and several experimental results show that the algorithm’s performance is validated.

Key words: curvelet, dual tree complex wavelet transform, total variation, texture, basis pursuit