Abstract
In the recent past considerable attention has been devoted to the application of Kalman filtering to smoothing out observation noise in image data. A generalization of the one-dimensional Kalman filter to two dimensions was earlier suggested by Habibi, but it has since been shown that this generalization is invalid since it does not preserve the optimality of the Kalman filter. A new method is proposed here that enables well-established Kalman-filter theory to yield a simple two-dimensional filter for images that can be modeled by two-dimensional wide-sense Markov (WSM) random fields.
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