摘要
模糊集理论是基于人的认知特性的,以模糊集理论为基础的图像处理算法具有较强的智能性和鲁棒性。本文使用隶属度对像素的污染程度进行刻画,从而得到一个基于模糊集的数字图像脉冲噪声滤波算法。与现有的一些经典算法相比,本文算法的滤波效果更好。最后通过实验仿真表明了该算法的可行性和有效性。
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.
出处
《微计算机信息》
北大核心
2008年第15期302-303,共2页
Control & Automation
关键词
数字图像
脉冲噪声
模糊集
Digital image
Impulse noise
Fuzzy sets
