This paper presents a new method for unsharp masking for contrast enhancement of images. The approach employs an adaptive filter that controls the contribution of the sharpening path in such a way that contrast enhancement occurs in high detail areas and little or no image sharpening occurs in smooth areas.

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This paper presents an adaptive step-size gradient adaptive filter. The step size of the adaptive filter is changed according to a gradient descent algorithm designed to reduce the squared estimation error during each iteration. An approximate analysis of the performance of the adaptive filter when its inputs are zero mean, white, and Gaussian and the set of optimal coefficients are time varying according to a randomwalk model is presented in the paper. The algorithm has very good convergence speed and low steady-state misadjustment. Furthermore, the tracking performance of these algorithms in nonstationary environments is relatively insensitive to the choice of the parameters of the adaptive filter and is very close to the best possible performance of the least mean square (LMS) algorithm for a large range of values of the step size of the stepsize adaptation algorithm. Several simulation examples demonstrating the good properties of the adaptive filter as well as verifying the analytical results are also presented in the paper.

This paper presents a review of polynomial filtering and, in particular, of tlie truncated Volterra filters. Following the introduction of the general properties of such filters, issues such as eficieiit realizations, design, adaptive algoritlims and stability are discussed.

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