Application of partition-based median type filters for suppressing noise in images

An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classif ied into one of M mutually exclusive partitions, each of which has a partic ular filtering operation. The observation signal space is partitioned based on the differences defined between the current pixel value and the outputs of CWM (center weighted median) biters with variable center weights. The e stimate at each location is formed as a linear combination of the outputs o f those CWM filters and the current pixel value. To control the dynamic ran ge of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (lea st mean square) algorithm, Recursive implementation of the new filter is th en addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and i t also works satisfactorily in reducing Gaussian noise as well as mixed Gau ssian and impulse noise.