Spatial filtering of two-dimensional pictorial data as an extension of one-dimensional filter theory is applied to the problem of enhancing the detection of localized objects which are superimposed upon a noisy background.

Four types of filters are derived. These are the linear, quadratic, general statistical, and decision filters. Each filter is of the "matched" type, the different designs being associated with various degrees of knowledge about the noise statistics.

A computer simulation of the linear and general statistical filters was done and examples are shown.

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