Abstract
A computationally efficient multiframe LMMSE filtering algorithm, the motion-compensated multiframe (MCMF) Wiener filter, for restoring image sequences that are degraded by both blur and noise is proposed. MCMF Wiener filter applies to the cases where each frame of the ideal image sequence can be expressed as a globally shifted version of its previous frame. As opposed to single-frame filtering, the MCMF Wiener filter accounts for interframe (temporal) correlations as well as intraframe (spatial) correlations in restoring a given image sequence. The MCMF filter requires neither the explicit estimation of cross correlations among the frames, nor any matrix inversion. It accounts for the interframe correlations implicitly by using the estimated interframe motion information. The results of an extensive study on the performance and robustness of the proposed algorithm are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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