基于决策分析的高椒盐噪声轮胎痕迹图像滤波方法

决策分析能准确判断出噪声像素与信号像素，均值滤波能较好平滑噪声，而自适应中值滤波能较好地保持原始图像的细节及边缘。为了恢复被高密度椒盐噪声污染的轮胎痕迹图像，提出三者相结合的新算法。该算法结合三者的优点，与传统中值滤波器、自适应中值滤波器等非线性滤波器相比，能得到更好的图像质量。实验表明，算法能有效消除灰度轮胎痕迹图像中的高密度椒盐噪声和彩色轮胎痕迹图像中的中低密度椒盐噪声，较好地保护了图像的细节及边缘信息。

Removal of high-density salt and pepper noise of tire trace images based on decision analysis algorithm

Decision analysis can accurately distinguish between corrupted pixels and signal pixels. Mean filters can smooth the noise well, while adaptive median filters can preserve the details and edge information of the original image. In order to restore the tire trace image with high-density impulse noise, a new fast and efficient algorithm is proposed. This algorithm combines the merits of decision analysis, mean filter and adaptive median filter. It shows significantly better image quality than nonlinear filters such as traditional median filter and adaptive median filter. Experimental results show that this algorithm can eliminate high-density impulse noise in the gray tire trace image and color tire trace image at noise level less than 60%, and it effectively preserves the details and edge information of the original image.