图象噪声的模糊聚类平滑方法

模糊C-means算法在聚类分析中已得到了成功的应用,本文提出一种利用模糊C-means算法消除噪声的新方法。一般来说,图象中的噪声点就是其灰度值与其周围象素的灰度值之差超过某个门限值的点。根据这个事实,首先利用模糊C-means算法分类,再利用标准核函数检测出噪声点,然后将噪声点去掉。由于只修改噪声点处的象素灰度值,而对于其它象素的灰度值不予改变,所以本算法能够很好地保护细节和边缘。本方法每次处理3×3个点,而以往的方法只能每次处理一个点,所以本方法能提高运算速度。文中给出了利用本方法对实际图象处理的结果,并与梯度倒数权值法进行了定量的比较。

The Fuzzy C-means algorithm has been successfully used for clustering of data in pattern recognition for a number of years. In this paper, a new noise smoothing scheme using the Fuzzy C-means algorithm is proposed. In general, a pixel is noise if the difference between it and its surrounding ones is greater than a given threshold. Based on this fact, first, we use Fuzzy C-means algorithm to cluster pixels in a 3 by 3 mask, then use a standard nucleus function (a window function) to find out noises in this area, and at last, replace the noise by the average of its surrounding area. Due to the advantage of this smoothing scheme that only noises are to be changed, the others remain unchanged, noises can be cleaned out very well and the edges and details well reserved. Nine pixels are processed simultaneously instead of only one every time. So the computation speed is fast. Results of the application of this scheme to several real world images, the evaluation of its performance, and a comparison between it and the Gradient Inverse Weighted Smoothing scheme are also presented.