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This paper proposes a new AR model-based restoration algorithm which is able to suppress mixed noise processes and recover lost signals in an image sequence. A drawback of an AR model is the limiting size of the block (of pixels) that can be adequately modeled. Using a single set of AR coefficients to restore large region of missing data will result in a… (More)
Gibbs-Markov random field (GMRF) modeling has been shown to be a robust method in the detection of missing-data in image sequences for a video restoration application. However, the maximum a posteriori probability (MAP) estimation of the GMRF model requires computationally expensive optimization algorithms in order to achieve an optimal solution. The… (More)
Bayesian motion estimation requires two pdf models: observation model and motion field (prior) model. The optimization process for this method uses sequential approach, e.g. simulated annealing. This paper proposes adaptive blocksize observation model and multiscale regularization for the prior model and the optimization process. The purposes are to… (More)
It is well known that accurate dense motion field can improve the video coding efficiency. This paper presents a novel Markov random field (MRF) model that estimates both the dense motion and uncovered background fields in image sequences, and the application of these estimates in H.263-based video coding framework.