Abstract :
Image sequence restoration has been steadily gaining
in importance with the increasing prevalence of visual digital
media. The demand for content increases the pressure on archives
to automate their restoration activities for preservation of the
cultural heritage that they hold. There are many defects that
affect archived visual material and one central issue is that of
Dirt and Sparkle, or “Blotches.” Research in archive restoration
has been conducted for more than a decade and this paper places
that material in context to highlight the advances made during
that time. The paper also presents a new and simpler Bayesian
framework that achieves joint processing of noise, missing data,
and occlusion.
Keywords :
Gibbs sampling , image processing , Marginalization , MarkovChain Monte Carlo , Motion estimation , noise reduction , video restoration. , Bayesian inference , Autoregressive models , factored sampling , film and video post production , compositionsampling , missing data reconstruction , Video processing