目的 在比较几种椒盐去噪方法的滤波窗口尺寸选择策略的基础上,提出一种基于自适应滤波窗的距离加权图像椒盐噪声滤除方法。方法 首先将图像中灰度值为0或255的像素点判定为噪声点,接着对每个噪声点,在以该噪声点为中心、不断增大面积的滤波窗口序列中,寻找包含非噪声点的最小尺寸窗口。若此窗口尺寸小于预设的阈值,则使用该窗口中的非噪声点进行距离加权滤波。否则认为该噪声点位置位于图像自身灰度值为0或255的像素点区域内部,使用少数服从多数策略计算灰度恢复值。结果 将本文方法与其他7种椒盐去噪方法相比较。当图像自身包含较多灰度值为0或255的像素点时,本文方法去噪效果优于其他7种方法。当图像自身不含或较少包含灰度值为0或255的像素点时,本文方法与其他方法中的最优去噪结果效果相当。结论 本文方法不仅能够有效滤除椒盐噪声,而且适用于自身包含灰度值为0或255的像素点多的椒盐噪声图像。

Objective After analyzing several schemes for determining filtering window size for the removal of salt-and-pepper noise, we propose a distance-weighted image denoising method based on adaptive filtering window. Method The proposed method first identifies the pixels with gray level 0 or 255 as noise pixels. Then, for each noise pixel, the minimum window with noise-free pixels is found. If the minimum window of a certain noise pixel is smaller than a given threshold, then noise-free pixels within the minimum window are used to perform distance-weighted filtering. Otherwise, the current noise pixel should be located in the regions composed of noise-free pixels with gray level 0 or 255, and a majority strategy is used to generate the restored gray level. Result The proposed method is evaluated by comparing it with seven other image denoising methods. Simulation results show that the new method achieves a better effect than its counterparts on images that contain noise-free pixels with gray level 0 or 255. Among all the compared methods, the new method achieves the best denoising effect on images that contain few or even no pixels with gray level 0 or 255. Conclusion The proposed method effectively removes salt-and-pepper noise and is also suitable for images that contain many noise-free pixels with gray level 0 or 255.