A System for Reconstruction of Missing Data in Image Sequences Using Sampled 3D AR Models and MRF Motion Priors

This paper presents a new technique for interpolating missing data in image sequences. A 3D autoregressive (AR) model is employed and a sampling based interpolator is developed in which reconstructed data is generated as a typical realization from the underlying AR process. rather than e.g. least squares (LS). In this way a perceptually improved result is achieved. A hierarchical gradient-based motion estimator, robust in regions of corrupted data, employing a Markov random field (MRF) motion prior is also presented for the estimation of motion before interpolation.

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