一种改进均值的自适应中值滤波算法

针对自适应中值滤波算法在滤除高浓度椒盐噪声和保留图像边缘细节中的不足,提出了一种改进均值的自适应中值滤波(IMAMF)算法。该算法采用扩充图像边界的方式,使得原图像的边界点能在自适应的滤波窗口下参与噪声检测和滤波处理,并在检测噪声和信号时,增加了噪声阈值判定,将存在噪声的像素点用修正后的均值滤波器值输出,信号点则用原始灰度值输出。为了验证算法的可行性,采用了5种不同的算法进行仿真对比分析,并从主观角度和客观指标上进行效果评价。试验结果表明:该算法能有效滤除浓度为10%～90%范围内的椒盐噪声,且图像细节和边缘信息得到了更好的保留,滤波性能明显优于其他算法。

An improved mean-valued method of adaptive median filter

To deal with the deficiency in terms of de-noising and preserving image details of the adaptive median filter,an improved mean-valued method of adaptive median filtering (IMAMF) is proposed.The algorithm adopts the way of extending edge of the image,so that all the pixels of the original image can be detected by noise detection factor and filtering under the adaptive window.When noise and signal are detected,the noise threshold is increased.If the pixels with noise are corrected,the new average filter value is output.Otherwise,the signal point is output with the original gray value.In order to verify the feasibility of the algorithm,five different algorithms are used for simulation analysis.From the subjective and objective angles of different algorithms for comparative analysis,the experimental results show that the algorithm can not only effectively filter out salt and pepper noise in the range of 10% to 90%,but also can better preserve the details and edge information of the original image.Obviously,the new method has better properties.