Recovery of polarimetric Stokes images by spatial mixture models.

A Bayesian approach for joint restoration and segmentation of polarization encoded images is presented with emphasis on both physical admissibility and smoothness of the solution. Two distinct models for the sought polarized radiances are used: (i) the polarized light at each site of the image is described by its Stokes vector, which directly follows a mixture of truncated Gaussians, explicitly assigning zero probability to inadmissible configurations and (ii) polarization at each site is represented by the coherency matrix, which is parameterized by a set of variables assumed to be generated by a spatially varying mixture of Gaussians. Application on real and synthetic images using the proposed methods assesses the pertinence of the approach.