用无人机巡检电力通道线路拍摄的图像具有较多的椒盐噪声, 针对传统的中值滤波和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, 提出了一种基于偏微分中值滤波算法。该算法通过计算原像素点四个方向的导数值是否大于设定的阈值, 分析原像素点是否为可疑噪声点。如果其中有一个导数值大于设定阈值就确定为可疑噪声点, 把窗口移动到导数值小的窗口再次求导, 如果还有导数值大于阈值就确定为噪声点, 确定噪声点后, 用已经设定好的掩膜和噪声点的领域窗口进行加权卷积求出新的像素值, 用新的像素值代替原噪声点。最终结果表明, 该算法比起自适应中值滤波算法有更高的峰值信噪, 去噪效果比较好。

The image taken by the unmanned aerial vehicle (UAV) patrol power channel line has more salt and pepper noise. For the shortcoming of traditional median filtering and adaptive median filtering algorithm to filter out the high concentration of salt and pepper noise and preserve the image edge details, a median filtering algorithm based on partial differential is proposed. The algorithm analyzes whether the original pixel point is a suspected noise point by calculating whether the derivative value of the four pixels in the four directions from the original pixel point is greater than a set threshold, and if one of the derivative value is greater than the set threshold, it is determined to be a suspicious noise point, and the window is moved to if the derivative value is larger than the threshold, it is determined as a noise point. After the noise point is determined, the weighted convolution is performed with the field window of the mask and the noise point that has been set to obtain a new pixel value. The original noise point is replaced by a new pixel value. The final result shows that the algorithm has higher peak signal noise than that of the adaptive median filtering algorithm, and the denoising effect is better.