模糊集理論是基于人的認(rèn)知特性的，以模糊集理論為基礎(chǔ)的圖像處理算法具有較強(qiáng)
的智能性和魯棒性。本文使用隸屬度對(duì)像素的污染程度進(jìn)行刻畫，從而得到一個(gè)基于模糊集的數(shù)字圖像脈沖噪聲濾波算法。與現(xiàn)有的一些經(jīng)典算法相比，本文算法的濾波效果更好。最后通過實(shí)驗(yàn)仿真表明了該算法的可行性和有效性。
關(guān)鍵詞：數(shù)字圖像；脈沖噪聲；模糊集
An Image Filter for Eliminating Impulse Noise Based on Fuzzy Sets Sun Zhong-gui College of Mathematics Science, Liaocheng University, Liaocheng 252059, China Abstract ： Fuzzy set theory, comparing with some other theories, can provide us with knowledge-based and robust tools for image processing. By computing the fuzziness of the pixels’ corrupted degree and taking corresponding filter parameters, a new image filter for impulse noise is presented in this paper. Comparing with the median filter, which is excellent for removing impulse noise, this new filter is more effective. In the end, simulation results show that the new algorithm is feasible.
Key Words：Digital image; Impulse noise; Fuzzy sets