Abstract
In this paper a new method to image restoration problems is proposed. The main result is the definition of an image model which does not require knowledgea priori of the image autocorrelation function or the use of identification estimation algorithms. The basic hypothesis is that the image can be modeled as an ensemble of two-dimensional differentiable surfaces. In this case the signal and its partial derivatives with respect to the spatial coordinates can be assumed to be the state vector and an image state space representation is directly obtained. A semicausal dependence model is adopted, it is embedded in a strip recursive scheme suitable to the Kalman filter application. Numerical results, concerning three different kinds of images, show high performances of the algorithm.
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