Practical, unified, motion and missing data treatment in degraded video
Recently, the problem of automated restoration of archived sequences has caught the attention of the Video Broadcast industry. One of the main problems is deadling with Blotches caused by film abrasion or dirt adhesion. This paper presents a new framework for the simultaneous treatment of missing data and motion in degraded video sequences. Using simple, translational models of motion, a joint solution for the detection, and reconstruction of missing data is proposed. The framework also incorporates the unique notion of dealing with occlusion and uncovering as it pertains to picture building. The idea is to use MCMC to solve the resulting problem articulated under a Bayesian framework, but to deploy purely deterministic mechanisms for dealing with the solution. This results in a relatively fast implementation that unifies many of the pixel-by-pixel schemes previously described in the literature.
video reconstruction statistical interpolation image reconstruction image restoration motion estimation gibbs sampling bayesian inference