Abstract
ABSTRACT In this paper, we present the output probability density functions of generalized open-closing (GOC) and generalizedcbs-opening (GCO) filters under the different input distributions such as uniform, Gaussian and biexponential distributions,and calculate their digital features (expectations and variances). Then, we apply these filters to restore an image corrupted by impulsive noise and further test their efficiency in noise-suppressing and detail-preserving characteristics. The simulation results show that the GOC and GCO filters have good performances.Key words: Morphological filters, Nonlinear filtering, Statistical properties, Image processing i.INTRODUCTION Morphological filters are nonlinear signal transformations that locally modify geometric features of signals. In recentyears, morphological filters have been successfully applied to the many fields of image analysis and processing includingbiomedical image processing, automated industrial inspection, shape recognition and image restoration ect.111.
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