In order to increase the performance of computational algorithms in terms of efficiency of estimators, we tested new nonparametric estimators in fuzzy and cellular automata models. In particular, image de-noising algorithms exist to restore digital images corrupted by impulse noise. These algorithms may do poorly in many common cases, for example, when high contrast and sharp edges lead to outliers, spikes, or non-symmetric patterns for neighborhing pixels. This would stem from the choices of estimators in the algorithms. We investigated new nonparametric estimators and compared to existing methods in simulation study. We detected better performances of our new methods under various situations.