Noncausal Estimation for Discrete Gauss-Markov Random Fields

  • Bernard C. Levy
Part of the Progress in Systems and Control Theory book series (PSCT, volume 3)


In [1], it was shown that 2-D discrete Gauss-Markov random fields can be characterized in terms of a noncausal nearest-neighbor model (NNM) driven by locally correlated noise. This result is used here to obtain a simple solution of the smoothing problem for Gauss-Markov random fields. It is shown that the smoother has a nearest-neighbor structure of the same type as the original field, and that the smoothing error is itself a Gauss-Markov random field. Since the operator describing the smoother dynamics is positive and self-adjoint, the smoother can be implemented by using efficient iterative algorithms for elliptic PDEs.


Image Restoration Gibbs Distribution Elliptic PDEs Boundary Process Reciprocal Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Birkhäuser Boston 1990

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  • Bernard C. Levy

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