Abstract
In this paper a parallel scheme is presented for the identification of two-dimensional noncausal image blurs. It is shown that the blur identification problem can be specified as a parallel set of one-dimensional complex autoregressive moving average (ARMA) identification problems. By expressing the ARMA models as equivalent infinite-order auto-regressive (AR) models, an entirely linear estimation procedure can be followed yielding minimum phase solutions. Further, it will be shown that under certain conditions (blur symmetry) it is possible to reconstruct a useful noncausal set of MA (blur) parameters from the identified minimum-phase set. Several identification results are given as examples.
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