Non Linear Image Processing With EHW Filter

Typical non-linear image processing applications include the correction of non-linear distortion introduced by components, communication channels and compensation for non-linearity in input output devices. Every digital signal or image processing operation can be viewed at its most basic level as the manipulation of a series of finite-length binary strings. Whether the operation is implemented on a processor through software or in dedicated hardware, the data and the algorithms are invariably mapped through electronic logic components, which are inherently binary in nature. In nonlinear image and signal processing, the design of operators is carried out by seeking the optimum mapping from one set of binary strings to another. This contrasts with the linear approach which formulates a solution by optimizing coefficients within a generalized multiply-accumulate context. Traditional adaptive filter works in a rectangular window whose size varies during filter operation depending on certain conditions. The optimal size of the window is crucial and influences the computation and memory requirements. However, this optimal size selection is highly application specific and its convergence from an initial value requires experimentation with various sizes of standard filters. Nonlinear image processing is presented in this paper as a generalization of the above operation by removing the linearity constraints. It seeks the optimum mapping implemented directly in logic. The linear solution should be viewed as a special case of the set of all logic based solutions rather than as an alternative. This generalization, the optimum nonlinear solution will be either better or equivalent to the linear solution, but it should not be worse. This inequality holds regardless of the problem or the criteria, provided that the training data is sufficient.