Fuzzy stack filters-their definitions, fundamental properties, and application in image processing

A new fuzzy filter, called fuuy stack filter (FSF), is proposed in this paper to extend the filtering capability of conventional stack filter (SF), which is based on the positive Boolean function (PBF) as its window operator. We fuzzify the onset and off-set of a given PBF to obtain two types of fuzzy PBF’s. Then, we adopt the architecture of threshold decomposition to develop this new fuzzy filter with a fuzzy PBF as its window operator. Each fuzzy PBF is associated with a set of control parameters. Therefore, the original PBF can be estimated from above and below by two fuzzy PBF’s with appropriate control parameters. Furthermore, we can apply the fuuy modifiers to modify the fuzzy PBF’s such that the PBF’s can be completely estimated by the fuzzy PBF’s. Hence, the stack filter is a special case of fuzzy stack filter. Since some control parameters are added in this new filter, the neural learning algorithms can be easily developed under the flexibility of the given control parameters. In this paper, we first propose the fuzzy (m,n) rank-order filter to test our proposed learning algorithm. In this simple learning algorithm, we can remove the noise-corrupted images very well in contrast to the filtering behavior of rank-order filters. We believe that the results presented in this paper will lead to more fruitful research on more advanced and powerful learning algorithms dedicated to the appropriate applications.