Recursive order-statistic soft morphological filters

In this paper, a new class of the recursive order-statistic soft morphological (ROSSM) filters are proposed, and their important properties related to the morphological filtering are developed. Criteria for optimal selection of parameters are provided in order to achieve excellent performance in noise reduction and edge preservation. It is shown through experimental results that the ROSSM filters, compared with the order-statistic soft morphological filters or other well-known nonlinear filters, have a better outcome in signal reconstruction. Two examples are used for the extension of the proposed filters to illustrate their flexibilty.