Title :
MCMC for joint noise reduction and missing data treatment in degraded video
Author :
Kokaram, Anil C. ; Godsill, Simon J.
Author_Institution :
Dept. of Electr. Eng., Trinity Coll., Dublin, Ireland
fDate :
2/1/2002 12:00:00 AM
Abstract :
Image sequence restoration has been steadily gaining importance with the increasing prevalence of visual digital media. Automated treatment of archived video material typically involves dealing with replacement noise in the form of "blotches" that have varying intensity levels and "grain" noise. In the case of replacement noise, the problem is essentially one of missing data that must be detected and then reconstructed based on surrounding spatio-temporal information, whereas the additive noise can be treated as a noise-reduction problem. It is typical to treat these problems as separate issues; however, it is clear that the presence of noise has an effect on the ability to detect missing data and vice versa. This paper therefore introduces a fully Bayesian specification for the problem that allows an algorithm to be designed that acknowledges and exploits the influences from each of the subprocesses, causing the observed degradation. Markov chain Monte Carlo (MCMC) methodology is applied to the joint detection and removal of both replacement and additive noise components. It can be seen that many of the previous processes presented for noise reduction and missing data treatment are special cases of the framework presented here
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; image enhancement; image restoration; image sequences; interference suppression; video signal processing; MCMC; Markov chain Monte Carlo methodology; additive noise; blotches; degraded video; fully Bayesian specification; grain noise; image sequence restoration; intensity levels; missing data treatment; noise reduction; noise-reduction problem; replacement noise; subprocesses; surrounding spatio-temporal information; Additive noise; Algorithm design and analysis; Bayesian methods; Degradation; Image reconstruction; Image restoration; Image sequences; Monte Carlo methods; Noise level; Noise reduction;
Journal_Title :
Signal Processing, IEEE Transactions on