Joint noise reduction, motion estimation, missing data reconstruction, and model parameter estimation for degraded motion pictures

Anil Christopher Kokaram; Simon J. Godsill

doi:10.1117/12.323801

22 September 1998 Joint noise reduction, motion estimation, missing data reconstruction, and model parameter estimation for degraded motion pictures

Anil Christopher Kokaram, Simon J. Godsill

Author Affiliations +

Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323801
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

Image sequence restoration has been steadily gaining in importance with the arrival of digital video broadcasting. Automated treatment of archived video material typically involves dealing with replacement noise in the form of 'blotches' with varying intensity levels and additive 'grain' noise. In the case of replacement noise the problem is essentially one of missing data which must be detected and then reconstructed based upon surrounding spatio- temporal information, while the additive noise can be treated as a noise reduction problem. This paper introduces a fully Bayesian specification of the problem, Markov chain Monte Carlo methodology is applied to the joint detection and removal of both replacement and additive noise components. The work presented builds upon the Bayesian image detection/interpolation methods developed in including now the ability to reduce noise in an image sequence as well as reconstruct the image intensity information within missing regions.

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Anil Christopher Kokaram and Simon J. Godsill "Joint noise reduction, motion estimation, missing data reconstruction, and model parameter estimation for degraded motion pictures", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323801

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