高斯模糊对高斯噪声有抑制作用
假设高斯函数是G(x),对于图像,假设高斯核是1*3的,则x是-1, 0,1,对应于G(-1),G(0)、G(1),sum=G(-1)+G(0)+G(1),则
G(-1)/sum + G(0)/sum + G(1)/sum = 1
import cv2 as cv
import numpy as np
def clamp(pv):
if pv > 255:
return 255
elif pv < 0:
return 0
else:
return pv
# 定义高斯噪声
def gaussian_noise(image):
h, w, c = image.shape
for row in range(h):
for col in range(w):
# 产生随机数,每次随机产生三个随机数,给R、G、B三个通道用
s = np.random.normal(0, 20, 3)
b = image[row, col, 0] # blue
g = image[row, col, 1] # green
r = image[row, col, 2] # red
image[row, col, 0] = clamp(b + s[0])
image[row, col, 1] = clamp(g + s[0])
image[row, col, 2] = clamp(r + s[0])
cv.imshow('noise image', image)
src = cv.imread('C:/Users/Y/Pictures/Saved Pictures/demo.png')
cv.namedWindow('input image', cv.WINDOW_AUTOSIZE)
cv.imshow('input image', src)
t1 = cv.getTickCount()
gaussian_noise(src)
t2 = cv.getTickCount()
time = (t2-t1)/cv.getTickFrequency()
print('time: %s ms' % (time*1000))
dst = cv.GaussianBlur(src, (5, 5), 15) # 5*5的卷积核
cv.imshow('Gaussian Blur', dst)
cv.waitKey(0)
cv.destroyAllWindows()