COMBINING DUAL-TREE WAVELET ANALYSIS AND PROXIMAL OPTIMIZATION FOR ANISOTROPIC SCALEFREE TEXTURE SEGMENTATION

The present work addresses the segmentation of textures characterized by anisotropy and scalefree statistics, two generic properties of use to model numerous real-world applications. This is achieved by proposing to combine a complex dualtree multiscale (wavelet) analysis within an inverse problem formulation aiming to estimate anisotropy and scalefree local parameters and to group them into piecewise homogeneous patches, jointly and in one single step. To minimize the corresponding functional, a primal-dual proximal convergent algorithm is devised and accelerated by taking advantage of the strong convexity of the data-fidelity term. Segmentation performance are assessed as function of the complexity of the task by means of Monte Carlo simulations conducted over synthetic textures, defined from anisotropic scalefree stochastic models.