Adaptive Fuzzy Filter and Its Application to Image Enhancement
Chang-Shing Lee, Yau-Hwang Kuo · Studies in fuzziness and soft computing · 2000
This chapter describes the design and evaluation of a novel adaptive fuzzy filter, and discusses its application to image enhancement. Most traditional edge detectors can perform well for uncorrupted images but are highly sensitive to impulse noise, so they can not work efficiently for blurred images. The proposed adaptive fuzzy filter consists of two major mechanisms: Adaptive Weighted Fuzzy Mean (AWFM) filter and Fuzzy Normed Inference System (FNIS) to realize the function of edge detection for smeared images. The membership functions of all fuzzy sets used in this filter can be adaptively determined for different images. Moreover, the adaptive fuzzy filter is capable of converting blurred edges to clear ones and suppressing noise at the same time. According to the experimental results, it works well in full range of random impulse noise probability and performs efficiently in the environment of mixed Gaussian impulse noise. This chapter also analytically evaluates the important properties of the filter to show its high performance in general cases.