Image Estimation And Missing Observations Reconstruction By Means Of A KALMAN Like Filter

M. Dirickx; A. Acheroy

doi:10.1117/12.970079

1 November 1989 Image Estimation And Missing Observations Reconstruction By Means Of A KALMAN Like Filter

M. Dirickx, A. Acheroy

Proceedings Volume 1199, Visual Communications and Image Processing IV; (1989) https://doi.org/10.1117/12.970079
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

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.

Citation Download Citation

M. Dirickx and A. Acheroy "Image Estimation And Missing Observations Reconstruction By Means Of A KALMAN Like Filter", Proc. SPIE 1199, Visual Communications and Image Processing IV, (1 November 1989); https://doi.org/10.1117/12.970079

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