Python实现图像去噪方式(中值去噪和均值去噪)

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 = 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

来源：https://blog.csdn.net/m0_37108612/article/details/90638237