基于分类和模糊滤波的X光图像椒盐噪声滤除算法

Salt and pepper noise filtering algorithm for X-ray images with multi-level classification and fuzzy filtering

为了解决当前椒盐噪声滤除算法对X光图像滤除效果不佳且运算效率不高的问题,提出了一种融合多级分类和自适应模糊滤波的椒盐噪声滤除方法,主要包括像素点多级分类和自适应模糊滤波两个部分。在像素点多级分类阶段,先结合先验知识设计快速的一级粗分类,将像素点分为椒盐噪声、信号和可疑噪声三类。对于可疑噪声,再提取区域内的直方图分布特征,设计BP神经网络分类器进行精确分类,最终将图像中的所有像素点分为信号和椒盐噪声两类。在自适应模糊滤波阶段,针对三种模糊集合分别创建模糊隶属度函数,计算模糊隶属度值,通过模糊加权求和恢复像素点亮度。实验结果表明,该方法的像素点分类正确率高,滤波后图像的峰值信噪比高,平均滤波耗时少。

In order to solve the problem that the current salt and pepper noise filtering algorithms for X-ray images are ineffective and low efficiency,this paper proposed a salt and pepper filtering algorithm with multi-level classification and adaptive fuzzy filtering,which included two parts,such as pixels’ multi-level classification and adaptive fuzzy filtering. In the process of multi-level classification,it designed a rapid rough classification according to priori knowledge,to divide the pixels into three categories: salt and pepper noise,signal and suspicious noise. For the suspicious noise,it extracted the histogram distribution features in the region,and designed the BP neural network classifier to classify the pixels,and finally classified all the pixels in the image into two kinds of signal and salt and pepper noise. In the process of adaptive fuzzy filtering,it created fuzzy membership function for three fuzzy sets,and calculated fuzzy membership value,and restored pixel brightness by fuzzy weighted summing. The experimental results show that the new method has high accuracy of pixel classification,high peak signal to noise ratio of filtered image,and less average time-consuming of filtering.