基于差分分层噪声检测的开关中值滤波算法

为了精确地检测出图像中的脉冲噪声并滤除，提出了一种差分分层噪声检测的开关中值滤波算法。该算法对噪声检测窗口内像素点按灰度值大小排序，通过差分方法划分出高、低阶噪声块和信号块3部分。当待测像素点属于信号块时视其为信号点；否则，视其为可能噪声点。利用可能噪声点与信号块中与其灰度值最临近的信号点的灰度的差定义了梯度函数，在梯度函数的基础上定义了用于对可能噪声点进行二次检测的模糊隶属函数，对滤波方法进行模糊加权，得到一种加权滤波方法。实验结果证明了该算法对脉冲噪声有很强的抑制作用。

Switching median filter with boundary discriminative noise detection

A switching median filter based on boundary discriminative noise detection is proposed to detect impulse noise precisely and denoise. All the pixels in the filtering window are sorted and divided into three groups：lower intensity impulse noise, higher intensity impulse noise, and uncorrupted pixels. If the considered pixel falls into uncorrupted pixels, it is viewed as a signal pixel without processed further, otherwise, it will be classified as a probable noise pixel. The fuzzy gradient function is defined by using the difference of the gray value of probable noise pixels and signal pixels which have the nearest gray value to probable noise pixels. The fuzzy membership function which is used to identify the real noise pixels from probable noise pixels is defined on the gradient function. By using this fuzzy membership function as the weight function, a new weighted median filter approach is proposed. Simulation results show the filter is effective to preserve impulse noise.