how to remove salt and pepper noise from images using python?

Question

I have tried to implement the following algorithm but the resulting image looks the same.

Step 1: Read Noisy Image.

Step 2: Select 2D window of size 3x3 with centre element as processing pixel. Assume that the pixel being processed is P ij .

Step 3: If P ij is an uncorrupted pixel (that is, 0< P ij <255), then its value is left unchanged.

Step 4: If P ij = 0 or P ij = 255, then P ij is a corrupted pixel.

Step 5: If 3/4 th or more pixels in selected window are noisy then increase window size to 5x5. Step 6: If all the elements in the selected window are 0‟s and 255‟s, then replace P ij with the mean of the elements in the window else go to step 7.

Step 7: Eliminate 0‟s and 255‟s from the selected window and find the median value of the remaining elements. Replace Pij with the median value.

Step 8: Repeat steps 2 to 6 until all the pixels in the entire image are processed.

Here is my code. Please suggest improvements.

import Image

im=Image.open("no.jpg")
im = im.convert('L')

for i in range(2,im.size[0]-2):
    for j in range(2,im.size[1]-2):
        b=[]
        if im.getpixel((i,j))>0 and im.getpixel((i,j))<255:
            pass
        elif im.getpixel((i,j))==0 or im.getpixel((i,j))==255:
            c=0
            for p in range(i-1,i+2):
                for q in range(j-1,j+2):
                    if im.getpixel((p,q))==0 or im.getpixel((p,q))==255: 
                        c=c+1
            if c>6:
                c=0
                for p in range(i-2,i+3):
                    for q in range(j-2,j+3):
                        b.append(im.getpixel((p,q)))
                        if im.getpixel((p,q))==0 or im.getpixel((p,q))==255:
                            c=c+1
                if c==25:
                    a=sum(b)/25
                    print a
                    im.putpixel((i,j),a)
                else:
                    p=[]
                    for t in b:
                        if t not in (0,255):
                            p.append(t)
                    p.sort()
                    im.putpixel((i,j),p[len(p)/2])
            else:
                b1=[]
                for p in range(i-1,i+2):
                    for q in range(j-1,j+2):
                        b1.append(im.getpixel((p,q)))
                im.putpixel((i,j),sum(b1)/9)

im.save("nonoise.jpg")   

Solution 1

You should use median filter, it is easy to implement and work very fine for salt and pepper noise.

Solution 2

As Olivier suggested, the median filter provides the best result.

Here is the code I generated for adding salt and pepper noise into an image. The code is for python with OpenCV 3.0.0 :

import numpy as np
import cv2

img = cv2.imread('3.jpg', 1)
row,col,ch = img.shape
p = 0.5
a = 0.009
noisy = img

  # Salt mode
num_salt = np.ceil(a * img.size * p)
coords = [np.random.randint(0, i - 1, int(num_salt))
          for i in img.shape]
noisy[coords] = 1

  # Pepper mode
num_pepper = np.ceil(a * img.size * (1. - p))
coords = [np.random.randint(0, i - 1, int(num_pepper))
          for i in img.shape]
noisy[coords] = 0

cv2.imshow('noisy', noisy)

Here is the code to use the median filter:

median_blur= cv2.medianBlur(noisy, 3)
cv2.imshow('median_blur', median_blur)  

cv2.waitKey()
cv2.destroyAllWindows()

The window used to blur the noisy image can be modified as per requirement.

Comments

[Admin]

Even though I have some pixels with values 0 and 255, the output image is the same as the input image.