A MAP estimation based segmentation model for speckled images

In this paper, we propose a new fuzzy-based variational model that efficiently computes partitioning of speckled images, such as images obtained from Synthetic Aperture Radar (SAR). The model is derived by using the so-called maximizing a posteriori (MAP) estimation method. The novelties of the model are: (1) the Gamma distribution rather than the classical Gaussian distribution is used to model the gray intensities in each homogeneous region of the images (Gamma distribution function is better suited for speckled images); (2) an adaptive weighted regularization term with respect to a fuzzy membership function is designed to protect the segmentation results from degeneration (being over-smoothed). Compared with the classical total variation (TV) regularizer, the proposed regularization term has a sparser property. In addition, a new alternative direction iteration algorithm is proposed to solve the model. The algorithm is efficient since it integrates the split Bregman method and the Chambolle's projection method. Numerical examples are given to verify the efficiency of our model.