采用改进的自适应模糊多级中值滤波算法去除牛肉图像斑点噪声

牛肉图像中的斑点噪声与肌内脂肪的颜色特征相似，要准确提取牛肉图像中的肌内脂肪颜色特征，就必须先对斑点噪声进行滤除。为此在自适应模糊多级中值滤波的基础上进行了改进：第1步，确定当前点是否为噪声点。先计算当前像素点与其邻域均值的差的绝对值，然后与阈值(阈值随当前点的灰度值而变化)相比较，如果绝对值大于阈值，则被认为是斑点噪声。第2步，对于斑点噪声，将采用自适应模糊多级中值滤波方法来滤除；如果不是，保留原像素点不变。试验结果表明，改进的算法在斑点噪声滤除与细节保护方面均优于标准的中值滤波、多级中值滤波以及形态滤波。

Removing the speckle noises in beef image using an improved adaptive fuzzy multilevel median filter algorithm

Speckle noises are similar to intramuscular fat pixels in beef image, and they must be removed if intramuscular fat pixels need to be correctly separated from beef muscle pixels. For removing the speckle noises more quickly and completely, an improved algorithm based on adaptive fuzzy multilevel median filter method was presented. The steps are as follows: first, confirming the selected pixel is a noise or not. As for this, the absolute gap between the pixel gray value and the mean gray value of its neighbors must be calculated firstly, and then it is compared with a changeable threshold which is related to the mean gray value. If the gap is bigger than the threshold, the selected pixel will be regarded as a speckle noise. Second, if the selected pixel is a speckle noise, adaptive fuzzy multilevel median filter will be used to remove it; if not, the pixel will not be changed. The experimental results show that the improved algorithm is superior to standard median filter, multilevel median filter and morphological filter methods in preserving the details and removing the speckle noises.