CODEWITHSUNDEEP
pythonpython-3.ximageimage-processingnoise
alyssaeliyah
alyssaeliyah

Reputation: 2244

How to add synthetic noise in an image with specified error probability

I want to create synthetic noise within an image. How will I degrade the black and white image with errors, with an independent probability of error at each point. How will I do that in Python (e.g. Error probability = 0.0011)?

enter image description here

Upvotes: 2

Views: 682

Answers (2)

nathancy
nathancy

Reputation: 46600

Here's a vectorized approach using OpenCV + skimage.util.random_noise. You can experiment with noise modes such as localvar, pepper, s&p, and speckle to obtain the desired result. You can set the proportion of noise with the amount parameter. Here's an example using s&p with amount=0.011:

enter image description here

import cv2
import numpy as np
from skimage.util import random_noise

# Load the image
image = cv2.imread('1.png', 0)

# Add salt-and-pepper noise to the image
noise = random_noise(image, mode='s&p', amount=0.011)

# The above function returns a floating-point image in the range [0, 1]
# so need to change it to 'uint8' with range [0,255]
noise = np.array(255 * noise, dtype=np.uint8)

cv2.imshow('noise',noise)
cv2.imwrite('noise.png',noise)
cv2.waitKey()

Upvotes: 2

Hymns For Disco
Hymns For Disco

Reputation: 8395

Here's an example program simply replacing the "degraded" pixels with black, using the Pillow library

from PIL import Image
import random

img = Image.open('text.png')
pixels = img.load()

for x in range(img.size[0]):
    for y in range(img.size[1]):
        if random.random() < 0.011:
            pixels[x,y] = 0 # only 1 number given since the image is grayscale

img.save('text_degraded.png')

I've increased the probability to 0.011 to make it more noticeable, here's the output enter image description here

Upvotes: 1

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