Recursive displacement estimation and restoration of noisy-blurred image sequences

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Abstract
A recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and intensity field from a noisy-blurred image sequence is developed. By simultaneously estimating these two fields, information is made available to each filter regarding the reliability of estimates that they are dependent upon. Nonstationary models are used for the DVF and the intensity field in the proposed estimator, thus avoiding the smoothing of boundaries present in both. The advantage of the proposed SDIE (simultaneous displacement and intensity field estimation) algorithm is that the error inherent in estimating the DVF is taken into account in the filtering of the intensity field. A second advantage is that, through the use of the nonstationary VCGM (vector coupled Gauss-Markov) and STCGM (spatiotemporal coupled Gauss-Markov) models, boundaries in both the DVF and the intensity fields are preserved. The performance of the proposed SDIE algorithm was shown to be superior to that of the Wiener-based PR algorithm and the 2-D Kalman filter in estimating the DVF and intensity field, respectively, from a noisy-blurred image sequence.

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