A massively parallel multi-level approach to a domain decomposition method for the optical flow estimation with varying illumination

We consider a variational method to solve the optical flow problem with varying illumination. We apply an adaptive control of the regularization parameter which allows us to preserve the edges and fine features of the computed flow. To reduce the complexity of the estimation for high resolution images and the time of computations, we implement a multi-level parallel approach based on the domain decomposition with the Schwarz overlapping method. The second level of parallelism uses the massively parallel solver MUMPS. We perform some numerical simulations to show the efficiency of our approach and to validate it on classical and real-world image sequences

Techniques variationnelles et calcul parallèle en imagerie : Estimation du flot optique avec luminosité variable en petits et larges déplacements

The work presented in this thesis focuses on the estimation of the optical flow through variational methods in small and large displacements. We propose a model based on the combined local-global strategy to which we add the consideration of brightness intensity variations. The particularity of this manuscript is the use of the finite element method to solve the equations. Indeed, for now, this method is really rare in the field of the optical flow. Thanks to this choice of resolution, we implement an adaptive control of the regularization and a mesh adaptation to refine the solution on the edges of the image. To reduce computation times, we parallelize the programs. The first method implemented is a parallel in time method called parareal. By combining a coarse and a fine solver, this algorithm speeds up the computations. To save even more time and to also be able to handle high resolution sequences, we then use a domain decomposition method. Combined with the massively parallel solver MUMPS, this method allows a significant reduction of computation times. Finally, we propose to couple the domain decomposition method and the parareal to have the benefits of both methods. In the second part, we apply all these models to the case of the optical flow estimation in large displacements. We use the parareal method to cope with the non-linearity of the problem. We end by a concrete example of application of the optical flow in film restoration.Le travail présenté dans cette thèse porte sur l'estimation du flot optique par méthodes variationnelles en petits et en grands déplacements. Nous proposons un modèle basé sur la combinaison locale-globale à laquelle nous ajoutons la prise en compte des variations de la luminosité. La particularité de ce manuscrit réside dans l'utilisation de la méthode des éléments finis pour la résolution des équations. En effet, cette méthode se fait pour le moment très rare dans le domaine du flot optique. Grâce à ce choix de résolution, nous proposons d'implémenter un contrôle local de la régularisation ainsi qu'une adaptation de maillage permettant d'affiner la solution au niveau des arêtes de l'image. Afin de réduire les temps de calcul, nous parallélisons les programmes. La première méthode implémentée est la méthode parallèle en temps appelée pararéel. En couplant un solveur grossier et un solveur fin, cet algorithme permet d'accélérer les calculs. Pour pouvoir obtenir un gain de temps encore plus important et également traiter les séquences en haute définition, nous utilisons ensuite une méthode de décomposition de domaine. Combinée au solveur massivement parallèle MUMPS, cette méthode permet un gain de temps de calcul significatif. Enfin, nous proposons de coupler la méthode de décomposition de domaine et le pararéel afin de profiter des avantages de chacune. Dans une seconde partie, nous appliquons tous ces modèles dans le cas de l'estimation du flot optique en grands déplacements. Nous proposons de nous servir du pararéel afin de traiter la non-linéarité de ce problème. Nous terminons par un exemple concret d'application du flot optique en restauration de films

Variational techniques and parallel computing in computer vision : Optical flow estimation with varying illumination in small and large displacements

Le travail présenté dans cette thèse porte sur l'estimation du flot optique par méthodes variationnelles en petits et en grands déplacements. Nous proposons un modèle basé sur la combinaison locale-globale à laquelle nous ajoutons la prise en compte des variations de la luminosité. La particularité de ce manuscrit réside dans l'utilisation de la méthode des éléments finis pour la résolution des équations. En effet, cette méthode se fait pour le moment très rare dans le domaine du flot optique. Grâce à ce choix de résolution, nous proposons d'implémenter un contrôle local de la régularisation ainsi qu'une adaptation de maillage permettant d'affiner la solution au niveau des arêtes de l'image. Afin de réduire les temps de calcul, nous parallélisons les programmes. La première méthode implémentée est la méthode parallèle en temps appelée pararéel. En couplant un solveur grossier et un solveur fin, cet algorithme permet d'accélérer les calculs. Pour pouvoir obtenir un gain de temps encore plus important et également traiter les séquences en haute définition, nous utilisons ensuite une méthode de décomposition de domaine. Combinée au solveur massivement parallèle MUMPS, cette méthode permet un gain de temps de calcul significatif. Enfin, nous proposons de coupler la méthode de décomposition de domaine et le pararéel afin de profiter des avantages de chacune. Dans une seconde partie, nous appliquons tous ces modèles dans le cas de l'estimation du flot optique en grands déplacements. Nous proposons de nous servir du pararéel afin de traiter la non-linéarité de ce problème. Nous terminons par un exemple concret d'application du flot optique en restauration de films.The work presented in this thesis focuses on the estimation of the optical flow through variational methods in small and large displacements. We propose a model based on the combined local-global strategy to which we add the consideration of brightness intensity variations. The particularity of this manuscript is the use of the finite element method to solve the equations. Indeed, for now, this method is really rare in the field of the optical flow. Thanks to this choice of resolution, we implement an adaptive control of the regularization and a mesh adaptation to refine the solution on the edges of the image. To reduce computation times, we parallelize the programs. The first method implemented is a parallel in time method called parareal. By combining a coarse and a fine solver, this algorithm speeds up the computations. To save even more time and to also be able to handle high resolution sequences, we then use a domain decomposition method. Combined with the massively parallel solver MUMPS, this method allows a significant reduction of computation times. Finally, we propose to couple the domain decomposition method and the parareal to have the benefits of both methods. In the second part, we apply all these models to the case of the optical flow estimation in large displacements. We use the parareal method to cope with the non-linearity of the problem. We end by a concrete example of application of the optical flow in film restoration

Coupling parareal and adaptive control in optical flow estimation with application in movie's restoration

In this paper we use a variational method in order to estimate the optical flow. We use a combined Local-Global strategy coupled with a local choice of the regularization term. In order to improve the computation time we have implemented a parallel in time algorithm. As application of the optical flow we present an example of image reconstruction

​A Space-Time Parallel Method for the Optic Flow Estimation in Large Displacements and Varying Illumination Case

International audienceWe consider a unified variational PDEs model to solve the optic flow problem for large displacements and varying illumination. Although, the energy functional is nonconvex and severely nonlinear, we show that the model offers a well suited framework to extend the efficient methods we used for small displacements. In particular, we resort to an adaptive control of the diffusion and the illumination coefficients which allows us to preserve the edges and to obtain a sparse vector field. We develop a combined space-time parallel programming strategy based on a Schwarz domain decomposition method to speed up the computations and to handle high resolution images, and the parareal algorithm, to enhance the speedup and to achieve a lowest-energy local minimum. This full parallel method gives raise to several iterative schemes and allows us to obtain a good balance between several objectives, e.g. accuracy, cost reduction, time saving and achieving the "best" local minimum. We present several numerical simulations to validate the different algorithms and to compare their performances

​A Space-Time Parallel Method for the Optic Flow Estimation in Large Displacements and Varying Illumination Case

International audienceWe consider a unified variational PDEs model to solve the optic flow problem for large displacements and varying illumination. Although, the energy functional is nonconvex and severely nonlinear, we show that the model offers a well suited framework to extend the efficient methods we used for small displacements. In particular, we resort to an adaptive control of the diffusion and the illumination coefficients which allows us to preserve the edges and to obtain a sparse vector field. We develop a combined space-time parallel programming strategy based on a Schwarz domain decomposition method to speed up the computations and to handle high resolution images, and the parareal algorithm, to enhance the speedup and to achieve a lowest-energy local minimum. This full parallel method gives raise to several iterative schemes and allows us to obtain a good balance between several objectives, e.g. accuracy, cost reduction, time saving and achieving the "best" local minimum. We present several numerical simulations to validate the different algorithms and to compare their performances