Locally adaptive techniques for stack filtering

This paper introduces a new structure for stack filtering, where the filter adapts to the local characteristics encountered in data. Both supervised and unsupervised techniques for optimal design are investigated. We split the image into small regions and select the stack filter to process each region according to the spatial domain or threshold level domain characteristics of the input signal. This method provides a significant improvement potential over the global stack filtering approach. Some local statistics are computed, to build a reduced input space which efficiently describes the most important local characteristics of data. Vector quantization is used for clustering the reduced input space into a small number of regions, and then finding a mapping between reduced input space clusters and the filter space, will result in rules for selecting the best suited stack filter for a given region. The supervised clustering procedures are shown to surpass significantly the global filtering approach.