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Optimization analysis of rank conditioned rank selection filters

Lukac R.

Journal of Applied Computer Science

2004

Vol. 12, nr 1

23-42

This paper focuses on she optimization analysis and robustness of a nonlinear filtering class of rank conditioned rank selection (RCRS) filters. which combine the general framework of rank selection filters and rank-order information on the selected input samples. Using the rank selection filter strategy, the output sample is restricted to be an order-statistic from the input set spawned by a sliding filtering window, while the number (known as the order of the filter) and file configuration of selected samples are used to extract she rank-order information lo determine the output ranked sample. By simple varying of the order and configuration of selected samples, the RCRS filler can be designed to perform a number of smoothing operations. As shown in this paper, the order and the configuration of the filter parameters influence the filter robustness, whereas the norm of the optimization criteria affects the RCRS filters in terms of a balance between noise attenuation and detail preserving characteristics.