Impulse, gaussian and salt and pepper noise with OpenCV

Question

I'm studying Image Processing on the famous Gonzales "Digital Image Processing" and talking about image restoration a lot of examples are done with computer-generated noise (gaussian, salt and pepper, etc). In MATLAB there are some built-in functions to do it. What about OpenCV?

Answer 1

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.

For example additive gaussian noise:

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);

Answer 2

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
    # aug = iaa.AdditiveGaussianNoise(loc=0, scale=0.1*255)

    # poisson noise
    # aug = iaa.AdditivePoissonNoise(lam=10.0, per_channel=True)

    # 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()

Answer 3

Simple Function to add Gaussian, Salt-pepper speckle and poisson noise to an image

Parameters
----------
image : ndarray
    Input image data. Will be converted to float.
mode : str
    One of the following strings, selecting the type of noise to add:

    'gauss'     Gaussian-distributed additive noise.
    'poisson'   Poisson-distributed noise generated from the data.
    's&p'       Replaces random pixels with 0 or 1.
    'speckle'   Multiplicative noise using out = image + n*image,where
                n,is uniform noise with specified mean & variance.

import numpy as np
import os
import cv2

def noisy(noise_typ,image):

if noise_typ == "gauss":
        row,col,ch= image.shape
        mean = 0
        #var = 0.1
       #sigma = var**0.5
        gauss = np.random.normal(mean,1,(row,col,ch))
        gauss = gauss.reshape(row,col,ch)
        noisy = image + gauss
        return noisy
    elif noise_typ == "s&p":
        row,col,ch = image.shape
        s_vs_p = 0.5
        amount = 0.004
        out = image
        # Salt mode
        num_salt = np.ceil(amount * image.size * s_vs_p)
        coords = [np.random.randint(0, i - 1, int(num_salt))
                  for i in image.shape]
        out[coords] = 1

        # Pepper mode
        num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
        coords = [np.random.randint(0, i - 1, int(num_pepper))
                  for i in image.shape]
        out[coords] = 0
        return out
    elif noise_typ == "poisson":
        vals = len(np.unique(image))
        vals = 2 ** np.ceil(np.log2(vals))
        noisy = np.random.poisson(image * vals) / float(vals)
        return noisy
    elif noise_typ =="speckle":
        row,col,ch = image.shape
        gauss = np.random.randn(row,col,ch)
        gauss = gauss.reshape(row,col,ch)        
        noisy = image + image * gauss
        return noisy

Answer 4

"Salt & Pepper" noise can be added in a quite simple fashion using NumPy matrix operations.

def add_salt_and_pepper(gb, prob):
    '''Adds "Salt & Pepper" noise to an image.
    gb: should be one-channel image with pixels in [0, 1] range
    prob: probability (threshold) that controls level of noise'''

    rnd = np.random.rand(gb.shape[0], gb.shape[1])
    noisy = gb.copy()
    noisy[rnd < prob] = 0
    noisy[rnd > 1 - prob] = 1
    return noisy

Answer 5

# Adding noise to the image    

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

img = cv2.imread('./fruit.png',0)
im = np.zeros(img.shape, np.uint8) # do not use original image it overwrites the image
mean = 0
sigma = 10
cv2.randn(im,mean,sigma) # create the random distribution
Fruit_Noise = cv2.add(img, im) # add the noise to the original image
plt.imshow(Fruit_Noise, cmap='gray')

The values of mean and sigma can be altered to bring about a specific change in noise like gaussian or pepper-salt noise etc. You can use either randn or randu according to the need. Have a look at the documentation: https://docs.opencv.org/2.4/modules/core/doc/operations_on_arrays.html#cv2.randu

Answer 6

I made some change of @Shubham Pachori 's code. When reading a image into numpy arrary, the default dtype is uint8, which can cause wrapping when adding noise onto the image.

import numpy as np
from PIL import Image

"""
image: read through PIL.Image.open('path')
sigma: variance of gaussian noise
factor: the bigger this value is, the more noisy is the poisson_noised image

##IMPORTANT: when reading a image into numpy arrary, the default dtype is uint8,
which can cause wrapping when adding noise onto the image. 
E.g,  example = np.array([128,240,255], dtype='uint8')
     example + 50 = np.array([178,44,49], dtype='uint8')
Transfer np.array to dtype='int16' can solve this problem.
"""


def gaussian_noise(image, sigma):
    img = np.array(image)
    noise = np.random.randn(img.shape[0], img.shape[1], img.shape[2])
    img = img.astype('int16')
    img_noise = img + noise * sigma
    img_noise = np.clip(img_noise, 0, 255)
    img_noise = img_noise.astype('uint8')
    return Image.fromarray(img_noise)


def poisson_noise(image, factor):
    factor = 1 / factor
    img = np.array(image)
    img = img.astype('int16')
    img_noise = np.random.poisson(img * factor) / float(factor)
    np.clip(img_noise, 0, 255, img_noise)
    img_noise = img_noise.astype('uint8')
    return Image.fromarray(img_noise)

Answer 7

http://scikit-image.org/docs/dev/api/skimage.util.html#skimage.util.random_noise

skimage.util.random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs)

Answer 8

#Adding noise
[m,n]=img.shape
saltpepper_noise=zeros((m, n));
saltpepper_noise=rand(m,n); #creates a uniform random variable from 0 to 1 
for i in range(0,m):
    for j in range(0,n):
        if saltpepper_noise[i,j]<=0.5:
            saltpepper_noise[i,j]=0
        else:
            saltpepper_noise[i,j]=255

Answer 9

although there is no built-in functions like in matlab imnoise(image,noiseType,NoiseLevel) but we can easily add required amount random valued impulse noise or salt and pepper into an image manually.

1. to add random valued impulse noise.

   import random as r
   def addRvinGray(image,n): # add random valued impulse noise in grayscale 
       '''parameters: 
            image: type=numpy array. input image in which you want add noise.
            n:  noise level (in percentage)'''
       k=0                  # counter variable 
       ih=image.shape[0]    
       iw=image.shape[1]
       noisypixels=(ih*iw*n)/100      # here we calculate the number of pixels to be altered.
   
       for i in range(ih*iw):
           if k<noisypixels:
                   image[r.randrange(0,ih)][r.randrange(0,iw)]=r.randrange(0,256) #access random pixel in the image gives random intensity (0-255)              
               k+=1
           else:
               break
       return image
   

2. to add salt and pepper noise

   def addSaltGray(image,n): #add salt-&-pepper noise in grayscale image
   
       k=0
       salt=True
       ih=image.shape[0]
       iw=image.shape[1]
       noisypixels=(ih*iw*n)/100
   
       for i in range(ih*iw):
            if k<noisypixels:  #keep track of noise level
                if salt==True:
                        image[r.randrange(0,ih)][r.randrange(0,iw)]=255
                        salt=False
                else:
                        image[r.randrange(0,ih)][r.randrange(0,iw)]=0
                        salt=True
                k+=1
            else:
                break
       return image
   

Note: for color images: first split image in to three or four channels depending on the input image using opencv function:

(B, G, R) = cv2.split(image)
(B, G, R, A) = cv2.split(image)

after spliting perform the same operations on all channels. at the end merge all the channels:

merged = cv2.merge([B, G, R])
return merged

Answer 10

def add_salt_noise(src, ratio: float = 0.05, noise: list = [0, 0, 0]):
    dst = src.copy()
    import random
    shuffle_dict = {}
    i = 0
    while i < (int(dst.shape[0]*dst.shape[1] * ratio)):
        x, y = random.randint(0, dst.shape[0] - 1), random.randint(0, dst.shape[1] - 1)
        if (x, y) in shuffle_dict:
            continue
        else:
            dst[x, y] = noise
            shuffle_dict[(x, y)] = 0
            i += 1
    return dst