如何更改图像中的噪点颜色？

问题描述

我正在尝试生成一个嘈杂的图像，如下所示：

我已经弄清楚了如何生成带有随机噪声的图像，但无法弄清楚如何改变噪声的颜色。

import numpy as np
import cv2
import matplotlib.pyplot as plt

img = cv2.imread('/home/pourya/face/os.jpg')[...,::-1]/255.0
noise =  np.random.normal(loc=-1,scale=1,size=img.shape)

# noise overlaid over image
noisy = np.clip((img + noise*1.7),1)
noisy2 = np.clip((img + noise*1.6),1)

# noise multiplied by image:
# whites can go to black but blacks cannot go to white
noisy2mul = np.clip((img*(1 + noise*1.5)),1)
noisy4mul = np.clip((img*(1 + noise*1.4)),1)

noisy2mul = np.clip((img*(1 + noise*1.8)),1)
noisy4mul = np.clip((img*(1 + noise*1.7)),1)

# noise multiplied by bottom and top half images,# whites stay white blacks black,noise is added to center
img2 = img*2
n2 = np.clip(np.where(img2 <= 1,(img2*(1 + noise*0.2)),(1-img2+1)*(1 + noise*0.2)*-1 + 2)/2,1)
n4 = np.clip(np.where(img2 <= 1,(img2*(1 + noise*0.4)),(1-img2+1)*(1 + noise*0.4)*-1 + 2)/2,1)


# norm noise for viz only
noise2 = (noise - noise.min())/(noise.max()-noise.min())
plt.figure(figsize=(20,20))
plt.imshow(np.vstack((np.hstack((img,noise2)),np.hstack((noisy,noisy2)),np.hstack((noisy2mul,noisy4mul)),np.hstack((n2,n4)))))
plt.show()
plt.hist(noise.ravel(),bins=100)
plt.show()

使用上面的代码，我得到了以下结果。

如何产生类似于第一张图像的单色噪声？ 谢谢

解决方法

这是在Python / OpenCV中执行此操作的一种方法。使用numpy作为蒙版创建灰度噪声图像。创建彩色图像。进行bitwise_and操作，将噪声和遮罩组合为omg和blue。

输入：

import cv2
import numpy as np

# load image and get dimensions
img = cv2.imread("zelda1.jpg")
hh,ww = img.shape[:2]

# create noise image (multiplier increase noise in result)
noise = (800*np.random.random((hh,ww))).clip(0,255).astype(np.uint8)

# make blue image
blue = np.full_like(img,(255,0))

# combine img and blue using noise as mask
img_masked = cv2.bitwise_and(img,img,mask=255-noise)
blue_masked = cv2.bitwise_and(blue,blue,mask=noise)
result = cv2.add(img_masked,blue_masked)
    
# write result to disk
cv2.imwrite("noise.jpg",noise)
cv2.imwrite("blue.jpg",blue)
cv2.imwrite("zelda1_blue_noise.jpg",result)

# display it
cv2.imshow("noise",noise)
cv2.imshow("blue",blue)
cv2.imshow("result",result)
cv2.waitKey(0)
cv2.destroyAllWindows()

噪声图像：

蓝色图片：

结果：