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 to this state vector, and one defining the initial condition. In this report 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 proposed 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 an image sequence. The main advantage of the resulting software is the use of a quality criteria on observations for weighting their contribution in the estimation process and of a dynamic model to ensure a relevant temporal regularity of the result.