Data-driven and learning-based strategies for the analysis, modeling and reconstruction of dynamical systems are currently emerging as promising research directions as an alternative to classic model-driven approaches for a wide variety of application fields, including atmosphere and ocean science, remote sensing, computer vision.... [2,3,4]. Especially, deep learning schemes [1] are currently investigated to address inverse problems, i.e. reconstruction of signals or images from observations. Especially, recent works [e.g., 3,4] have shown that one can learn variational models and solvers for the reconstruction.

This internship will specifically investigate the development of deep learning inverse models for SAR imaging to retrieve sea surface geophysical parameters (e.g., wave fields, sea surface winds,..). Based on a review of the state-of-the-art [eg, 3,4,5], the goal will to propose, implement and evaluate different deep learning schemes. Case-studies with groundtruthed real SAR imaging datasets will be considered and implemented.

Keywords: deep learning, inverse problems, variational models, SAR imaging, ocean remote sensing.