Image estimation and missing observations reconstruction by means of a KALMAN like filter

The purpose of the presented method is the noise reduction and the estimation of missing frames in interlaced images. In the case all the frames are present, there are two possible semi-causal optimal Kalman filters whose equations are only reducible if the image formation can be described by a first order separable Markov process: the first filter is causal in the direction of the rows and non causal in the direction of the columns, the second filter is causal in the direction of the columns and non causal in the direction of the rows. In the case of interlaced images with one missing frame, only one of two lines is observed and only the second Kalman filter is reducible.