实现对图像进行简单的高斯去噪和椒盐去噪。代码如下：

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import random
import scipy.misc
import scipy.signal
import scipy.ndimage
from matplotlib.font_manager import FontProperties
font_set = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=10)def medium_filter(im, x, y, step):sum_s = []for k in range(-int(step / 2), int(step / 2) + 1):for m in range(-int(step / 2), int(step / 2) + 1):sum_s.append(im[x + k][y + m])sum_s.sort()return sum_s[(int(step * step / 2) + 1)]def mean_filter(im, x, y, step):sum_s = 0for k in range(-int(step / 2), int(step / 2) + 1):for m in range(-int(step / 2), int(step / 2) + 1):sum_s += im[x + k][y + m] / (step * step)return sum_sdef convert_2d(r):n = 3# 3*3 滤波器, 每个系数都是 1/9window = np.ones((n, n)) / n ** 2# 使用滤波器卷积图像# mode = same 表示输出尺寸等于输入尺寸# boundary 表示采用对称边界条件处理图像边缘s = scipy.signal.convolve2d(r, window, mode='same', boundary='symm')return s.astype(np.uint8)def convert_3d(r):s_dsplit = []for d in range(r.shape[2]):rr = r[:, :, d]ss = convert_2d(rr)s_dsplit.append(ss)s = np.dstack(s_dsplit)return sdef add_salt_noise(img):# rows, cols, dims = img.shape#R = np.mat(img[:, :, 0])# G = np.mat(img[:, :, 1])#B = np.mat(img[:, :, 2])rows, cols= img.shapeR = np.mat(img[:, :])G = np.mat(img[:, :])B = np.mat(img[:, :])Grey_sp = R * 0.299 + G * 0.587 + B * 0.114Grey_gs = R * 0.299 + G * 0.587 + B * 0.114snr = 0.9noise_num = int((1 - snr) * rows * cols)for i in range(noise_num):rand_x = random.randint(0, rows - 1)rand_y = random.randint(0, cols - 1)if random.randint(0, 1) == 0:Grey_sp[rand_x, rand_y] = 0else:Grey_sp[rand_x, rand_y] = 255#给图像加入高斯噪声Grey_gs = Grey_gs + np.random.normal(0, 48, Grey_gs.shape)Grey_gs = Grey_gs - np.full(Grey_gs.shape, np.min(Grey_gs))Grey_gs = Grey_gs * 255 / np.max(Grey_gs)Grey_gs = Grey_gs.astype(np.uint8)# 中值滤波Grey_sp_mf = scipy.ndimage.median_filter(Grey_sp, (7, 7))Grey_gs_mf = scipy.ndimage.median_filter(Grey_gs, (8, 8))# 均值滤波Grey_sp_me = convert_2d(Grey_sp)Grey_gs_me = convert_2d(Grey_gs)plt.subplot(321)plt.title('加入椒盐噪声',fontproperties=font_set)plt.imshow(Grey_sp, cmap='gray')plt.subplot(322)plt.title('加入高斯噪声',fontproperties=font_set)plt.imshow(Grey_gs, cmap='gray')plt.subplot(323)plt.title('中值滤波去椒盐噪声（8*8）',fontproperties=font_set)plt.imshow(Grey_sp_mf, cmap='gray')plt.subplot(324)plt.title('中值滤波去高斯噪声（8*8）',fontproperties=font_set)plt.imshow(Grey_gs_mf, cmap='gray')plt.subplot(325)plt.title('均值滤波去椒盐噪声',fontproperties=font_set)plt.imshow(Grey_sp_me, cmap='gray')plt.subplot(326)plt.title('均值滤波去高斯噪声',fontproperties=font_set)plt.imshow(Grey_gs_me, cmap='gray')plt.show()def main():img = np.array(Image.open('E:/pycharm/GraduationDesign/Test/testthree.png'))add_salt_noise(img)if __name__ == '__main__':main()

  效果如下