how to remove salt and pepper noise from images using python?
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")
你应该使用中值滤波器，它很容易实现，并且对于盐和胡椒噪声工作得非常好。
What does your input image look like? You algorithm assumes only pixel value 0 and 255 is noise. If your noisy pixels actually have values other than that, your algorithm will not do anything and you might see output looks identical to input.
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.
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