On Variational Methods for Motion Compensated Inpainting
This work addresses video restoration for applications like archival or surveillance, but it appears incremental as it builds on standard variational methods without claiming major breakthroughs.
The paper tackles the problem of jointly estimating motion and recovering missing data in damaged video sequences using a Bayesian framework, resulting in a multiresolution algorithm for inpainting with experimental validation on synthetic and real sequences.
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 http://image.diku.dk/francois/seqinp.php.