图片素材

img

人为给出噪声

随机100个噪声点

代码

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
import cv2 as cv
import numpy as np

img = cv.imread("img.png", 1)
(rows, cols, chn) = img.shape
cv.imshow("img.png", img)

# 加噪声
for i in range(100):
x = np.random.randint(0, rows)
y = np.random.randint(0, cols)
img[x, y, :] = 255


cv.imshow("noise", img)
cv.imwrite("img_noise.png", img)
cv.waitKey()
cv.destroyAllWindows()

结果

2

噪声检测

代码

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
# python3.8
# utf-8
"""
1.将图片缩小二分之一:(这里只给出了生成部分,没有给出检测部分)
取左上角的点
2.找出噪声点:
划分方法一
划分方法二
处理特殊点
标记次数
3.标记
将噪声点的像素值标记为0
"""
import cv2 as cv
import numpy as np


class PixelChannel:
def __init__(self, channel, noise, row, col):
self.noise = noise
self.channel = channel
self.row = row
self.col = col


class Part:
def __init__(self, x, y, area):
self.x = x
self.y = y
self.area = area


# 将图像缩小到一半
def half(pixel_channel):
row = pixel_channel.row
col = pixel_channel.col
half_row = row // 2
half_col = col // 2
result = PixelChannel()
result.row = half_row
result.col = half_col
for i in range(row, 2):
for j in range(col, 2):
result.channel[i // 2][j // 2] = pixel_channel.channel[i][j]
return result


# 建立像素通道类
def create_pixel_channel(img_channel):
(row, col) = img_channel.shape
result = PixelChannel(img_channel, noise_check(img_channel), row, col)
return result


# 不考虑位置
def division1(_8area):
result = [0] * 8
p1 = [_8area[0][0], _8area[0][1], _8area[0][2], _8area[1][2], _8area[2][2], _8area[2][1], _8area[2][0],
_8area[1][0]]
p = sorted(p1)
q = [abs(int(p[0]) - int(p[1])), abs(int(p[1]) - int(p[2])), abs(int(p[2]) - int(p[3])), abs(int(p[3]) - int(p[4])),
abs(int(p[4]) - int(p[5])), abs(int(p[5]) - int(p[6])), abs(int(p[6]) - int(p[7]))]
# 判断区分度
if max(q) < 10:
return result, False
max_index = q.index(max(q))
for i in range(0, max_index + 1):
result[p1.index(p[i])] = 1
for i in range(8):
if result[i] != 1:
result[i] = 2
"""
min_index = 0
for i in range(1, 8):
if p[i] < p[min_index]:
min_index = i
max_index1 = min_index
for i in range(1, 8):
if p[i] > p[max_index1]:
max_index1 = i
max_index2 = min_index
for i in range(1, 8):
if i != max_index1 and p[i] > p[max_index2]:
max_index2 = i
max_index3 = min_index
for i in range(1, 8):
if i != max_index1 and i != max_index2 and p[i] > p[max_index3]:
max_index3 = i
max_index4 = min_index
for i in range(1, 8):
if i != max_index1 and i != max_index2 and i != max_index3 and p[i] > p[max_index4]:
max_index4 = i
# 将较小的的四个标记为1区域,较大区域标记为2
result[max_index1] = 2
result[max_index2] = 2
result[max_index3] = 2
result[max_index4] = 2
for i in range(8):
if result[i] != 2:
result[i] = 1
"""
return result, True


# 考虑位置
def division2(_8area):
result = [0] * 8
p = [_8area[0][0], _8area[0][1], _8area[0][2], _8area[1][2],
_8area[2][2], _8area[2][1], _8area[2][0], _8area[1][0]]
d = [int(p[0]) - int(p[1]), int(p[1]) - int(p[2]), int(p[2]) - int(p[3]), int(p[3]) - int(p[4]),
int(p[4]) - int(p[5]), int(p[5]) - int(p[6]), int(p[6]) - int(p[7]), int(p[7]) - int(p[0])]
max_index = 0
min_index = 0
for i in range(1, 8):
if d[i] > d[max_index]:
max_index = i
if d[i] < d[min_index]:
min_index = i
if max_index == min_index:
pass
elif max_index > min_index:
for i in range(0, min_index + 1):
result[i] = 1
for i in range(min_index + 1, max_index + 1):
result[i] = 2
if max_index < 7:
for i in range(max_index + 1, 8):
result[i] = 1
elif max_index < min_index:
for i in range(0, max_index + 1):
result[i] = 2
for i in range(max_index + 1, min_index + 1):
result[i] = 1
if min_index < 7:
for i in range(min_index + 1, 8):
result[i] = 2
# 判断区分度
max1 = 0
for i in range(8):
if result[i] == 1 and p[i] > max1:
max1 = p[i]
min2 = 0
for i in range(8):
if result[i] == 2 and p[i] < min2:
min2 = p[i]
if max1 - min2 < 10:
return result, False
else:
return result, True


# 一维数组映射到二维
def _1d_2_2d(x):
if x == 0:
return 0, 0
elif x == 1:
return 0, 1
elif x == 2:
return 0, 2
elif x == 3:
return 1, 2
elif x == 4:
return 2, 2
elif x == 5:
return 2, 1
elif x == 6:
return 2, 0
elif x == 7:
return 1, 0


# 特殊值处理
def special_check(_8area):
p = [_8area[0][0], _8area[0][1], _8area[0][2], _8area[1][2],
_8area[2][2], _8area[2][1], _8area[2][0], _8area[1][0]]
p_sum = int(p[0])+int(p[1])+int(p[2])+int(p[3])+int(p[4])+int(p[5])+int(p[6])+int(p[7])
max_differ = 0
max_index = 0
for i in range(8):
if abs(int(p[i]) - (p_sum - int(p[i])) // 7) > max_differ:
max_differ = abs(int(p[i]) - (p_sum - int(p[i])) // 7)
max_index = i
if max_differ > 10:
return _1d_2_2d(max_index)
else:
return -1, -1


def noise_check(img_channel):
(row, col) = img_channel.shape
result = [[0] * col for i in range(row)]
for i in range(1, row - 1):
for j in range(1, col - 1):
# _8_area = type(img_channel)
_8_area = [[0] * 3 for i in range(3)]
_8_area[0][0] = img_channel[i - 1][j - 1]
_8_area[0][1] = img_channel[i - 1][j]
_8_area[0][2] = img_channel[i - 1][j + 1]
_8_area[1][2] = img_channel[i][j + 1]
_8_area[2][2] = img_channel[i + 1][j + 1]
_8_area[2][1] = img_channel[i + 1][j]
_8_area[2][0] = img_channel[i + 1][j - 1]
_8_area[1][0] = img_channel[i][j - 1]
# 不考虑位置
part1, flag1 = division1(_8_area)
# 考虑位置
part2, flag2 = division2(_8_area)
if flag1 == False or flag2 == False:
continue
# 处理特殊值
sx, sy = special_check(_8_area)
if sx != -1:
result[i - 1 + sx][j - 1 + sy] += 1
# 比较
for k in range(8):
if part1[k] != part2[k]:
x, y = _1d_2_2d(k)
result[i - 1 + x][j - 1 + y] += 1
return result


def mark(pixel_channel):
for i in range(pixel_channel.row):
for j in range(pixel_channel.col):
if pixel_channel.noise[i][j] >= 6:
pixel_channel.channel[i][j] = 0
return pixel_channel


def main():
# 图像地址
img_address = "img_noise.png"
# 以BGR方式读入图像
img = cv.imread(img_address, 1)
cv.imshow("img_noise.png", img)
# 通道分离
channel_b, channel_g, channel_r = cv.split(img)
# 建立像素通道类
b = create_pixel_channel(channel_b)
g = create_pixel_channel(channel_g)
r = create_pixel_channel(channel_r)

fp = open('b.noise.csv', 'w')
for i in range(b.row):
for j in range(b.col):
print(b.noise[i][j], file=fp, end='')
print(",", file=fp, end='')
print("", file=fp)

fp = open('b.pixel.csv', 'w')
for i in range(b.row):
for j in range(b.col):
print(b.channel[i][j], file=fp, end='')
print(",", file=fp, end='')
print("", file=fp)

new_img = cv.merge((mark(b).channel, mark(g).channel, mark(r).channel))
cv.imwrite("denoised_img.png", new_img)
cv.imshow("denoised_img.png", new_img)
cv.waitKey()
cv.destroyAllWindows()


if __name__ == '__main__':
main()

结果

3

如下图所示,红框内表示没有被标记出的噪声点

denoised_img