Generalized Wiener Filtering Computation Techniques

Authors:
W. K. Pratt
Affiliations:
Department of Electrical Engineering, University of Southern California
Venue:
IEEE Transactions on Computers
Year:
1972

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Abstract

The classical signal processing technique known as Wiener filtering has been extended to the processing of one-and two-dimensional discrete data by digital operations with emphasis on reduction of the computational requirements. In the generalized Wiener filtering process a unitary transformation, such as the discrete Fourier, Hadamard, or Karhunen-Loéve transform is performed on the data that is assumed to be composed of additive signal and noise components. The transformed data is then modified by a filter function, and the inverse transformation is performed to obtain the discrete system output. The filter function is chosen to provide the best mean square estimate of the signal portion of the input data.