Solving Ill-posed Problems Using Data Assimilation. Application to optical flow estimation

Dominique Béréziat (1) , Isabelle Herlin (2, 3)

1 PEQUAN - Performance et Qualité des Algorithmes Numériques
2 Clime - Coupling environmental data and simulation models for software integration
3 CEREA - Centre d'Enseignement et de Recherche en Environnement Atmosphérique

Data Assimilation is a mathematical framework used in environmental sciences to improve forecasts performed by meteorological, oceanographic or air quality simulation models. Data Assimilation techniques require the resolution of a system with three components: one describing the temporal evolution of a state vector, one coupling the observations and the state vector, and one defining the initial condition. In this article, we use this framework to study a class of ill-posed Image Processing problems, usually solved by spatial and temporal regularization techniques. A generic approach is defined to convert an ill-posed Image Processing problem in terms of a Data Assimilation system. This method is illustrated on the determination of optical flow from a sequence of images. The resulting software has two advantages: a quality criterion on input data is used for weighting their contribution in the computation of the solution and a dynamic model is proposed to ensure a significant temporal regularity on the solution.

Domaines: Modélisation et simulation

Dominique Béréziat, Isabelle Herlin. Solving Ill-posed Problems Using Data Assimilation. Application to optical flow estimation. VISAPP - International Conference on Computer Vision Theory and Applications, Feb 2009, Lisboa, Portugal. pp.594-602. ⟨inria-00567444⟩