Recursive linear smoothing of two-dimensional random fields

In an earlier paper, recursive formulas for the causal filtering of two-dimensional random fields were developed. "Causality" in two dimensions is not a physical constraint but rather an artifact introduced to generate recursion, which in turn is motivated by computational efficiency. The earlier results are extended here in order to derive some recursive formulas for "smoothing" estimators which use all the data rather than just the data in the "past".