Inference on Gibbs optic-flow priors : Application to atmospheric turbulence characterization

In this paper, Bayesian inference is used to select the most evident Gibbs prior model for motion estimation given some image sequence. The proposed method supplements the maximum a posteriori motion estimation scheme proposed in He¿as et al. (2008). Indeed, in this recent work, the authors have introduced a family of multiscale spatial priors in order to cure the ill-posed inverse motion estimation problem. We propose here a second level of inference where the most likely prior model is optimally chosen given the data by maximization of Bayesian evidence. Model selection and motion estimation are assessed on Meteorological Second Generation (MSG) image sequences. Selecting from images the most evident multiscale model enables the recovery of physical quantities which are of major interest for atmospheric turbulence characterization.