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Parameter reduction for the compound Gauss-Markov model

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1 Author(s)
Jun Zhang ; Dept. of Electr. Eng. & Comput. Sci., Wisconsin Univ., Milwaukee, WI

The efficacy of the compound Gauss-Markov (CGM) model, initially proposed by Jeng and Woods (1990), has been demonstrated in several image processing applications. However, parameter estimation for the CGM model is difficult since it is not clear as to how the constraints or interdependence amongst the model parameters can be incorporated into the estimation procedures. As result, the parameter estimates tend to be inconsistent. It is shown that, under some reasonable symmetry constraints, the 80 interdependent parameters of the CGM model can be reduced to seven independent ones. This guarantees the consistency of model parameters obtained from parameter estimation algorithms, thereby removing a main obstacle for the parameter estimation of the CGM model

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Image Processing, IEEE Transactions on  (Volume:4 ,  Issue: 3 )