Python实现图像去噪方式(中值去噪和均值去噪)

Python  /  管理员 发布于 7年前   145

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 npfrom PIL import Imageimport matplotlib.pyplot as pltimport randomimport scipy.miscimport scipy.signalimport scipy.ndimagefrom matplotlib.font_manager import FontPropertiesfont_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 = 0  for 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_s  def convert_2d(r):  n = 3  # 3*3 滤波器, 每个系数都是 1/9  window = 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 s  def add_salt_noise(img):  rows, cols, dims = img.shape  R = np.mat(img[:, :, 0])  G = np.mat(img[:, :, 1])  B = np.mat(img[:, :, 2])   Grey_sp = R * 0.299 + G * 0.587 + B * 0.114  Grey_gs = R * 0.299 + G * 0.587 + B * 0.114   snr = 0.9   noise_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] = 0    else:      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

以上这篇Python实现图像去噪方式(中值去噪和均值去噪)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。