More On Image Noise Generation

More On Image Noise

More On Adding Noise in An Image

"Any fool can throw a stong down a well, but it takes a wise man to git it out"

It is always easiter to destroy(or critisize) than to build (or to create). This post is to document good noise functions I found on internet

In [1]:
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from scipy.ndimage import filters
from scipy import misc

# Ensure plots embeded in notebook
%matplotlib inline
In [2]:
# Source of the code is based on an excelent piece code from stackoverflow
# http://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv

def noise_generator (noise_type,image):
"""
Generate noise to a given Image based on required noise type

Input parameters:
image: ndarray (input image data. It will be converted to float)

noise_type: string
'gauss' Gaussian-distrituion based noise
'poission' Poission-distribution based noise
's&p' Salt and Pepper noise, 0 or 1
'speckle' Multiplicative noise using out = image + n*image
where n is uniform noise with specified mean & variance
"""
row,col,ch= image.shape
if noise_type == "gauss":
mean = 0.0
var = 0.01
sigma = var**0.5
gauss = np.array(image.shape)
gauss = np.random.normal(mean,sigma,(row,col,ch))
gauss = gauss.reshape(row,col,ch)
noisy = image + gauss
return noisy.astype('uint8')
elif noise_type == "s&p":
s_vs_p = 0.5
amount = 0.004
out = image
# Generate Salt '1' noise
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] = 255
# Generate Pepper '0' noise
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_type == "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_type =="speckle":
gauss = np.random.randn(row,col,ch)
gauss = gauss.reshape(row,col,ch)
noisy = image + image * gauss
return noisy
else:
return image

In [3]:
# Open Image
plt.figure(0)
im = misc.face()
plt.imshow(im)
plt.axis('off')
plt.show()
plt.close(0)
#print im
In [5]:
plt.figure(1)
sp_im = noise_generator('s&p', im)
plt.imshow(sp_im)
plt.axis('off')
plt.show()
plt.close(1)
#print sp_im
In [6]:
plt.figure(2)
gauss_im = noise_generator('gauss', im)
plt.subplot(1,2,1)
plt.title('Salt & Pepper Noise')
plt.imshow(sp_im)
plt.subplot(1,2,2)
plt.imshow(gauss_im)
plt.title('Gaussian Noise')
plt.show()
plt.close(2)

Published: September 09 2016

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