# Impulse, gaussian and salt and pepper noise with OpenCV

As far as I know there are no convenient built in functions like in Matlab. But with only a few lines of code you can create those images yourself.

Mat gaussian_noise = img.clone();
randn(gaussian_noise,128,30);


Salt and pepper noise:

Mat saltpepper_noise = Mat::zeros(img.rows, img.cols,CV_8U);
randu(saltpepper_noise,0,255);

Mat black = saltpepper_noise < 30;
Mat white = saltpepper_noise > 225;

Mat saltpepper_img = img.clone();
saltpepper_img.setTo(255,white);
saltpepper_img.setTo(0,black);


There is function random_noise() from the scikit-image package. It has several builtin noise patterns, such as gaussian, s&p (for salt and pepper noise), possion and speckle.

Below I show an example of how to use this method

from PIL import Image
import numpy as np
from skimage.util import random_noise

im = Image.open("test.jpg")
# convert PIL Image to ndarray
im_arr = np.asarray(im)

# random_noise() method will convert image in [0, 255] to [0, 1.0],
# inherently it use np.random.normal() to create normal distribution
# and adds the generated noised back to image
noise_img = random_noise(im_arr, mode='gaussian', var=0.05**2)
noise_img = (255*noise_img).astype(np.uint8)

img = Image.fromarray(noise_img)
img.show() There is also a package called imgaug which are dedicated to augment images in various ways. It provides gaussian, poissan and salt&pepper noise augmenter. Here is how you can use it to add noise to image:

from PIL import Image
import numpy as np
from imgaug import augmenters as iaa

def main():
im = Image.open("bg_img.jpg")
im_arr = np.asarray(im)

# gaussian noise

# poisson noise

# salt and pepper noise
aug = iaa.SaltAndPepper(p=0.05)

im_arr = aug.augment_image(im_arr)

im = Image.fromarray(im_arr).convert('RGB')
im.show()

if __name__ == "__main__":
main()