一种改进的极值均值自适应滤波算法

为了在滤除噪声的同时保持图像细节，提出了一种新型自适应滤波算法。该算法根据图像中的某点是否为邻域极值点，将全部像素分为可疑噪声和信号两类，若中心点属于可疑噪声，将其并入信号类组成一新类，并计算其均值与信号类均值的差值，再通过与阈值比较，确定是否用信号类的中值取代原噪声图像的值。滤波窗口能根据噪声密度自适应改变大小。实验表明，该算法具有良好的滤波性能，尤其在噪声严重时，效果优于其他中值滤波算法。

Improved adaptive filter based on extremum and mean

For keeping the details of an image when removing noise,a novel adaptive filter is proposed.According to whether the gray value of a pixel is its neighborhood extremum,all pixels are divided into two types,doubtful noise and signals.If the center pixel is doubtful noise,the signals,together with the center pixel,are recognized as a new type.By comparing the difference between the median of the signals and the new type with the threshold,it can be decided that if the original gray value should be instead by the median of the signals.The size of the window can change adaptively along with the density of the noise.In simulation experiment,it is proved that the filter has an excellent performance and is more effective than ordinary median filter especially on high noisy rate situations.