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22 September 1998 Bayesian framework for reconstructing missing data in color image sequences
Steven Armstrong, Anil Christopher Kokaram, Peter J. W. Rayner
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Proceedings Volume 3459, Bayesian Inference for Inverse Problems; (1998) https://doi.org/10.1117/12.323806
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States
Abstract
This paper presents a Bayesian framework for reconstructing missing regions of a color image sequence. Because the three color channels are not independent a multichannel median image model is chosen. Since the model extends through time to previous and following frames it incorporates motion estimation to compensate for the effects of motion in the original scene. The paper discusses methods for detecting the missing data which exploit the temporally uncorrelated nature of typical degradation. A Markov chain Monte Carlo Gibb's Sampling scheme is adopted for drawing samples for the missing data. The method draws these from the full posterior distributions for the missing data in each of the YUV color channels. The nature of the model means that the multivariate probability distributions for the missing data are difficult to sample from. The paper shows how this can be overcome with a numerical approach to the sampling. The efficiency of this approach relies on the fact that there are only a small and finite number of values that the data can take.
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Steven Armstrong, Anil Christopher Kokaram, and Peter J. W. Rayner "Bayesian framework for reconstructing missing data in color image sequences", Proc. SPIE 3459, Bayesian Inference for Inverse Problems, (22 September 1998); https://doi.org/10.1117/12.323806
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KEYWORDS
Data modeling
Digital filtering
Autoregressive models
Motion models
Image filtering
Motion detection
RGB color model
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