【摘要】 提出了一种新的利用局部统计信息 (极值 )的自适应中值滤波方法——极值中值滤波算法 .该方法可以有效地去除图象中的椒盐噪声 ,并保留图象的细节 .本文首先给出了一个噪声判别标准 ,然后描述了滤波算法的执行过程 ,对本算法与标准中值滤波算法 ,以及近几年出现的几种改进型中值滤波算法进行了分析与对比 ,最后给出了一组实验数据 .实验结果表明 ,与其他算法相比 ,本算法执行速度快 ,去除噪声与保留细节的效果好更多还原

【Abstract】 A new median based filtering algorithm-extremum median filtering is presented. In order not to perturb the efficient signals as much as possible when the noises are removed, the following approaches are developed in this paper. First, all the pixels are separated into signal pixels and noise pixels according to the decision criterion given in the following; then, noise pixels are replaced with the median value of their neighborhood in the input image. The decision criterion: if a pixel value is the extremum (max or min) of its neighborhood, it is a noise pixel; else, it is a signal pixel. This decision criterion is under such an assumption: inherent relationships exist among neighbor pixels. If a pixel value is far higher or lower than the others’ value of its neighborhood are, that is to say, a pixel has lower correlation with its neighbors, we may consider that it had been contaminated with noise. Else, if it is similar to the others, we consider that it represents an effective signal. Experimental results show that the assumption fits the facts quit well.In this paper, attention is forcused on filtering of images degraded by "salt and pepper" noises. Examples on images containing 184×148 pixels are given.Experimental results show that the EM filtering has better performance than standard median filtering with less subtle details being eliminated. The SNR of the image filtered with EM filter is about 4dB higher than that with median filter. This is because the operation only affects noise pixels and most of the uncontaminated pixels keep intact. Especially,in the case of lower SNR,larger filtering window improves the SNR notably. Median filter is not the case, for the filtering operation blurs the image extremely with the increasing of the filtering window.更多还原