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Solving ill-posed Image Processing problems using Data Assimilation

Abstract : 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.
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Contributor : Dominique Béréziat Connect in order to contact the contributor
Submitted on : Wednesday, March 18, 2009 - 11:59:03 AM
Last modification on : Wednesday, October 26, 2022 - 8:16:17 AM
Long-term archiving on: : Wednesday, September 22, 2010 - 12:20:18 PM


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  • HAL Id : inria-00368890, version 2


Dominique Béréziat, Isabelle Herlin. Solving ill-posed Image Processing problems using Data Assimilation. [Research Report] RR-6879, INRIA. 2009, pp.33. ⟨inria-00368890v2⟩



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