Joint interpolation of multi-sensor sea surface geophysical fields using non-local and statistical priors

This work addresses the joint analysis of multi-source and multi-resolution remote sensing data for the interpolation of high-resolution geophysical fields. As case-study application, we consider the interpolation of sea surface temperature fields. We propose a novel statistical model, which combines two key features: an exemplar-based prior and second-order statistical priors. The exemplar-based prior, referred to as a non-local prior, exploits similarities between local patches (small field regions) to interpolate missing data areas from previously observed exemplars. This non-local prior also sets an explicit conditioning between the multi-sensor data. Two complementary statistical priors, namely a prior on the spatial covariance and a prior on the marginal distribution of the high-resolution details, are considered as sea surface geophysical fields are expected to depict specific spectral and marginal features in relation to the underlying turbulent ocean dynamics. We report experiments on both synthetic data and real SST data. These experiments demonstrate the contributions of the proposed combination of non-local and statistical priors to interpolate visually-consistent and geophysically-sound SST fields from multi-source satellite data. We further discuss the key features and parameterizations of this model as well as its relevance with respect to classical interpolation techniques

Random walk models for geometry-driven image super-resolution

International audienceThis paper addresses stochastic geometry-driven im- age models and its application to super-resolution issues. Whereas most stochastic image models rely on some priors on the distribution of grey-level configurations (e.g., patch- based models, Markov priors, multiplicative cascades,...), we here focus on geometric priors. We aim at simulating tex- ture samples while controlling high-resolution geometrical features. In this respect, we introduce a stochastic model for texture orientation fields stated as a 2D Orstein-Uhlenbeck process. We show that this process resorts in the stationary case to priors on orientation statistics. We exploit this model to state image super-resolution as a geometry-driven vari- ational minimization, where the geometry is sampled from the proposed conditional 2D Orstein-Uhlenbeck process. We demonstrate the relevance of this approach for real images as- sociated with the remote sensing of ocean surface dynamics