GMLOS and a comparative study of nonlinear filters

The pre- and post-processing units for digital image filtering, are an essential part of any integrated vision or imaging system which uses an intensity image as input. These kinds of processing are normally multiple criteria optimization problems that may involve both restoration and enhancement of the degraded images. The most commonly used figures of merit for evaluating these filters are noise attenuation edge preservation, detail presentation and edge enhancement properties. In recent years, nonlinear techniques have been extensively used to achieve these tasks. However, none of these filters have shown to satisfy all of the above requirements. In this work we introduce a new nonparametric robust nonlinear filter based on generalized maximum likelihood reasoning and order statistics (GMLOS). A qualitative and quantitative comparison of GMLOS and other efficient nonlinear filters is presented to illustrate the capability of this filter in satisfying the desired requirements