Image sequence restoration and deinterlacing by motion-compensated Kalman filtering

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Abstract
In this paper we address the following two problems: (1) restoration of noisy and blurred progressively scanned image sequences, and (2) simultaneous restoration and de-interlacing of noisy and blurred interlaced image sequences. De-interlacing refers to the conversion of an interlaced image sequence to a progressive one. We first formulate a Kalman filtering algorithm for image sequence restoration, and then apply this algorithm to the simultaneous de-interlacing and restoration problem. To use Kalman filtering for image sequence restoration effectively, the temporal information contained in the image sequence needs to be incorporated into the problem formulation. One method of quantifying the new information inherent in an image sequence is to estimate the motion between successive frames of the sequence. We use this method, and incorporate the motion information into the Kalman filter through the observation equation. To restore frame k of an image sequence, an autoregressive (AR) model for the k'th frame is used, along with observations coming from both frame k and a previously restored and motion compensated frame k-1.

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