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新的去噪方法
选取 3X3 噪声点
中心点为 C，周围八个点分别是 A1~A8
处理中心点 C
对九个点进行不考虑位置的区域划分，再去掉中心点 C，对其余八个点进行不考虑位置的划分，若两次划分有明显的变化，那么说明中心点为噪声点。
如果中心点 C 为噪声，那么就在八邻域中找到与中心点最为接近的点的值，赋给中心点 C
处理八邻域
将九个点全排列，分成两个区域，然后搜索每个点，如果该点没有与所在区域的其他点相邻，那么就将它的噪声指数加一
效果
下面尝试用 opencv 中的 canny 算子检测噪声图和处理图（去噪后）的效果：
代码示例
python# python3.8
# utf-8
"""
1. 找出噪声点：新划分方法
标记次数
2. 修改
找最接近的未标记过的点
"""
import cv2 as cv
import numpy as np
threshold = 1
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
# 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
# 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
elif x == 8:
return 1, 1
# 二维数组映射到一维
def _2d_2_1d(x, y):
if x == 0 and y == 0:
return int(0)
elif x == 0 and y == 1:
return int(1)
elif x == 0 and y == 2:
return int(2)
elif x == 1 and y == 2:
return int(3)
elif x == 2 and y == 2:
return int(4)
elif x == 2 and y == 1:
return int(5)
elif x == 2 and y == 0:
return int(6)
elif x == 1 and y == 0:
return int(7)
elif x == 1 and y == 1:
return int(8)
def division1(_3x3):
result = [0] * 9
p1 = [_3x3[0][0], _3x3[0][1], _3x3[0][2], _3x3[1][2], _3x3[2][2], _3x3[2][1], _3x3[2][0],
_3x3[1][0], _3x3[1][1]]
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])), abs(int(p[7]) - int(p[8]))]
# 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(9):
if result[i] != 1:
result[i] = 2
return result, True
# 去除 del_num 号元素，del_num:0~8
def division2(_3x3, del_num):
p1 = [_3x3[0][0], _3x3[0][1], _3x3[0][2], _3x3[1][2], _3x3[2][2], _3x3[2][1], _3x3[2][0],
_3x3[1][0], _3x3[1][1]]
p2 = []
for i in range(9):
if i == del_num:
continue
p2.append(p1[i])
result = [0] * 9
p = sorted(p2)
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]) - 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
result[del_num] = 0
return result, True
# 搜索八邻域像素点是否合理 num:0~7
def check_8area(_8area, part, num):
x, y = _1d_2_2d(num)
if 0 <= x-1 <= 2 and 0 <= y-1 <= 2 and part[_2d_2_1d(x-1, y-1)] == part[num]:
pass
elif 0 <= x-1 <= 2 and 0 <= y <= 2 and part[_2d_2_1d(x-1, y)] == part[num]:
pass
elif 0 <= x-1 <= 2 and 0 <= y+1 <= 2 and part[_2d_2_1d(x-1, y+1)] == part[num]:
pass
elif 0 <= x <= 2 and 0 <= y+1 <= 2 and part[_2d_2_1d(x, y+1)] == part[num]:
pass
elif 0 <= x+1 <= 2 and 0 <= y+1 <= 2 and part[_2d_2_1d(x+1, y+1)] == part[num]:
pass
elif 0 <= x+1 <= 2 and 0 <= y <= 2 and part[_2d_2_1d(x+1, y)] == part[num]:
pass
elif 0 <= x+1 <= 2 and 0 <= y-1 <= 2 and part[_2d_2_1d(x+1, y-1)] == part[num]:
pass
elif 0 <= x <= 2 and 0 <= y-1 <= 2 and part[_2d_2_1d(x, y-1)] == part[num]:
pass
else:
return False
return True
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):
_3X3 = [[0] * 3 for i in range(3)]
_3X3[0][0] = img_channel[i - 1][j - 1]
_3X3[0][1] = img_channel[i - 1][j]
_3X3[0][2] = img_channel[i - 1][j + 1]
_3X3[1][2] = img_channel[i][j + 1]
_3X3[2][2] = img_channel[i + 1][j + 1]
_3X3[2][1] = img_channel[i + 1][j]
_3X3[2][0] = img_channel[i + 1][j - 1]
_3X3[1][0] = img_channel[i][j - 1]
_3X3[1][1] = img_channel[i][j]
# 不考虑位置
part1, flag1 = division1(_3X3)
# 如果在不去除点的情况下，不需要划分，那么就跳过
if not flag1:
continue
# 判断中心点是否合群
part_del_c, flag_del_c = division2(_3X3, 8)
# 如果去掉中心点后可划分，那么比较两种划分
if flag_del_c:
cnt = 0
for m in range(9):
if part_del_c[m] != 0 and part1[m] != part_del_c[m]:
cnt += 1
if cnt >= 1:
x, y = _1d_2_2d(9-1)
result[i - 1 + x][j - 1 + y] += 1
# 如果不可划分，那么中心点为可能的噪声
else:
x, y = _1d_2_2d(9 - 1)
result[i - 1 + x][j - 1 + y] += 1
# 搜索其余八邻域像素点是否合理
for k in range(8):
if not check_8area(_3X3, part1, k):
x, y = _1d_2_2d(k)
result[i - 1 + x][j - 1 + y] += 1
return result
# 不考虑缩小二分之一图像
def mark(pixel_channel):
for i in range(pixel_channel.row):
for j in range(pixel_channel.col):
if pixel_channel.noise[i][j] >= threshold:
pixel_channel.channel[i][j] = find_best(pixel_channel, i, j)
return pixel_channel
# 找到最合适的值
def find_best(pixel_channel, x, y):
p = pixel_channel.channel[x][y]
# 建立领域列表
neighborhood = []
if pixel_channel.row > x - 1 >= 0 and pixel_channel.col > y - 1 >= 0 and pixel_channel.noise[x - 1][y - 1] < threshold:
neighborhood.append(pixel_channel.channel[x - 1][y - 1])
if pixel_channel.row > x - 1 >= 0 and pixel_channel.col > y >= 0 and pixel_channel.noise[x - 1][y] < threshold:
neighborhood.append(pixel_channel.channel[x - 1][y])
if pixel_channel.row > x - 1 >= 0 and pixel_channel.col > y + 1 >= 0 and pixel_channel.noise[x - 1][y + 1] < threshold:
neighborhood.append(pixel_channel.channel[x - 1][y + 1])
if pixel_channel.row > x >= 0 and pixel_channel.col > y + 1 >= 0 and pixel_channel.noise[x][y + 1] < threshold:
neighborhood.append(pixel_channel.channel[x][y + 1])
if pixel_channel.row > x + 1 >= 0 and pixel_channel.col > y + 1 >= 0 and pixel_channel.noise[x + 1][y + 1] < threshold:
neighborhood.append(pixel_channel.channel[x + 1][y + 1])
if pixel_channel.row > x + 1 >= 0 and pixel_channel.col > y >= 0 and pixel_channel.noise[x + 1][y] < threshold:
neighborhood.append(pixel_channel.channel[x + 1][y])
if pixel_channel.row > x + 1 >= 0 and pixel_channel.col > y - 1 >= 0 and pixel_channel.noise[x + 1][y - 1] < threshold:
neighborhood.append(pixel_channel.channel[x + 1][y - 1])
if pixel_channel.row > x >= 0 and pixel_channel.col > y - 1 >= 0 and pixel_channel.noise[x][y - 1] < threshold:
neighborhood.append(pixel_channel.channel[x][y - 1])
# 搜索与给定点最接近的非噪声点
# 建立差值列表
d = []
for i in range(len(neighborhood)):
d.append(abs(int(neighborhood[i]) - int(p)))
if len(d) == 0:
return p
min_index = d.index(min(d))
return neighborhood[min_index]
# 考虑缩小二分之一图像
def repair(pixel_channel, half_channel):
# 左上角
for i in range(half_channel[0].row):
for j in range(half_channel[0].col):
if half_channel[0].noise[i][j] >= threshold:
pixel_channel.noise[i * 2][j * 2] += half_channel[0].noise[i][j]
# 右上角
for i in range(half_channel[1].row):
for j in range(half_channel[1].col):
if half_channel[1].noise[i][j] >= threshold:
pixel_channel.noise[i * 2][j * 2 + 1] += half_channel[1].noise[i][j]
# 左下角
for i in range(half_channel[2].row):
for j in range(half_channel[2].col):
if half_channel[2].noise[i][j] >= threshold:
pixel_channel.noise[i * 2 + 1][j * 2] += half_channel[2].noise[i][j]
# 右下角
for i in range(half_channel[3].row):
for j in range(half_channel[3].col):
if half_channel[3].noise[i][j] >= threshold:
pixel_channel.noise[i * 2 + 1][j * 2 + 1] += half_channel[3].noise[i][j]
for i in range(pixel_channel.row):
for j in range(pixel_channel.col):
if pixel_channel.noise[i][j] >= threshold:
pixel_channel.channel[i][j] = find_best(pixel_channel, i, j)
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)
# 缩小二分之一
# half_b = [half_1(b), half_2(b), half_3(b), half_4(b)]
# half_g = [half_1(g), half_2(g), half_3(g), half_4(g)]
# half_r = [half_1(r), half_2(r), half_3(r), half_4(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)
# 不考虑二分之一图像
new_img = cv.merge((mark(b).channel, mark(g).channel, mark(r).channel))
# 考虑二分之一图像
# new_img = cv.merge((repair(b, half_b).channel, repair(g, half_g).channel, repair(r, half_r).channel))
cv.imwrite("denoised_img.png", new_img)
cv.imshow("denoised_img.png", new_img)
cv.waitKey()
cv.destroyAllWindows()
if __name__ == '__main__':
main()正文完
