A data-parallel approach to the implementation of weighted medians technique on parallel/super-computers

Nonlinear filters have been used in many signal processing applications, for example, to obtain optimum signal extraction or detection in the presence of random noise. The weighted median filter (WMF), of which the standard median is a special case, is a novel nonlinear technique designed for 2D image processing. A major advantage of the WMF is its flexibility in design to deal with a wide variety of properties. This paper describes a commonly used class W(4,4,1) of the WMF. AS with most nonlinear methods, the computational demands of this technique are high and require a non-trivial number of "expensive" operations. A data parallel approach for efficient implementation of the WMF is described and implemented on two architecturally dissimilar supercomputers, the Convex C3840 and the Connection Machine CM-200. An analysis of the performance obtained from these two high performance parallel platforms is presented