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

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。

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