A synthesis of an optimal fuzzy filter based on local statistics
Hironori Takashima, Akin Taguchi, Yutaka Murata · Electronics and Communications in Japan (Part III Fundamental Electronic Science) · 1995
Abstract This paper presents a design method of data‐dependent filters that uses simplified fuzzy inference. Since the antecedents of fuzzy inference can comprise several local characteristics (i.e., observation values), it is possible for the fuzzy filter to adjust its weights to adapt to local image data. the tuning of membership functions and fuzzy rules of the proposed filter results in a least mean square (LMS)‐like algorithm. Thus, local characteristics can be increased for the proposed fuzzy fiter optimally. This paper, introduces a new observation value (calculated from local statistics) into the proposed filter. the proposed filter changes filter behavior according to the local properties of signals and provides good noise attenuation in all regions of image, including detail regions, while still preserving the details.