Adaptive α-trimmed mean filters with excellent detail-preserving

Adaptive α-trimmed mean filters based on local statistics of the signal are introduced in this paper. The resulting filter is then time/space-varying, allowing it to adapt to different parts of the signal. e.g. it can effectively remove background noise from smooth areas of the signal. While preserving edges (with different filter parameters) in detail areas. In order to further improve the filtering performance (detail preserving ability). adaptive α-trimmed mean filtering structures have been combined with adaptive center weighted median operations, exploiting temporal information which is lost by ordering in α-trimmed mean filtering. Simulation results are included to assess the performance of the proposed adaptive structures