python+opencv 高斯模糊

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()