原理（冈萨雷斯第四版p235）：

代码：

import cv2
import numpy as np
from matplotlib import pyplot as plt# for gray image
def auto_median_filter(img, S_max):output = img.copy()def class_B(z_xy, z_med, z_max, z_min):if (z_min < z_xy) & (z_xy < z_max):return z_xyelse:return z_medS_ori = 3copy = cv2.copyMakeBorder(img, *[S_ori // 2] * 4, borderType=cv2.BORDER_DEFAULT)  # 补零for i in range(img.shape[0]):for j in range(img.shape[1]):def class_A(copy, S_ori=3):board = S_ori // 2z_med = np.median(copy[i:i + board, j:j + board])z_max = np.max(copy[i:i + board, j:j + board])z_min = np.min(copy[i:i + board, j:j + board])if (z_min < z_med) & (z_med < z_max):output[i, j] = class_B(copy[i, j], z_med, z_max, z_min)else:# next_size = S_ori + 2next_size = cv2.copyMakeBorder(copy, *[1] * 4, borderType=cv2.BORDER_DEFAULT)  # 增尺寸S_ori = S_ori + 2if S_ori <= S_max:return class_A(next_size, S_ori)else:output[i, j] = z_medclass_A(copy, 3)return output.astype(np.uint8)def label_def(): # 定义坐标数字字体及大小plt.xticks(fontproperties='Times New Roman', size=8)plt.yticks(fontproperties='Times New Roman', size=8)if __name__ == '__main__':img = cv2.imread('D://pictures//Fig0335(a)(ckt_board_saltpep_prob_pt05).tif', 0)img_median = cv2.medianBlur(img, 7)out = auto_median_filter(img, 7)plt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = Falseplt.subplot(131), plt.imshow(img, "gray"), plt.title('椒盐噪声', fontsize='small'), label_def()plt.subplot(132), plt.imshow(img_median, "gray"), plt.title('中值滤波', fontsize='small'), label_def()plt.subplot(133), plt.imshow(out, "gray"), plt.title('自适应中值滤波', fontsize='small'), label_def()plt.show()