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• W2891678257 abstract "We develop in this paper a generic Bayesian framework for the joint estimation of motion and recovery of missing data in a damaged video sequence. Using standard maximum a posteriori to variational formulation rationale, we derive generic minimum energy formulations for the estimation of a reconstructed sequence as well as motion recovery. We instantiate these energy formulations and from their Euler-Lagrange Equations, we propose a full multiresolution algorithms in order to compute good local minimizers for our energies and discuss their numerical implementations, focusing on the missing data recovery part, i.e. inpainting. Experimental results for synthetic as well as real sequences are presented. Image sequences and extra material is available at this http URL." @default.
• W2891678257 created "2018-09-27" @default.
• W2891678257 creator A5055800699 @default.
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• W2891678257 date "2018-09-21" @default.
• W2891678257 modified "2023-09-27" @default.
• W2891678257 title "On Variational Methods for Motion Compensated Inpainting." @default.
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• W2891678257 hasPublicationYear "2018" @default.
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