基于相关权值的图像椒盐噪声自适应窗滤波

Self-adaptive Filtering of Salt-pepper Noise in Images with Correlation Weights

为有效滤除图像中椒盐噪声,提出一种基于相关权值的自适应窗滤波算法。算法基于极值检测判断噪声点并仅对噪声点滤波。引入灰度差刻画邻域像素与中心像素的相关性,以此为基础设置像素权值,对中心像素执行加权均值滤波。通过邻域窗口的自适应扩展适应噪声密度变化,并对邻域像素分区域设置权值,从而适应高椒盐噪声的滤除。仿真结果表明,本文算法能够有效滤除图像中的椒盐噪声,尤其在高椒盐噪声下性能表现更佳。

A self-adaptive weighted mean algorithm is proposed for filtering the salt-pepper noise in images. The algorithm detects the noise pixel with minimum-maximum inspection, and then replaces the noise pixel with weighted mean value, where the weight of each pixel in neighborhood is set based on its correlation with the center pixel. The algorithm adapts itself to different noise densities by rec- tifying the filtering window according to the number of non-extremum pixels in neighborhood. Simulation results showed that, compared with other methods, the proposed algorithm achieves more satisfactory images while it gives better Signal-to-Noise Ratio （SNR） and Mean Squared Error（ MSE）, and exhibits more excellent in scenarios where the image is highly corrupted.