A list,

Neighborhood averaging and median filtering belong to the category of spatial image smoothing.

Neighborhood averaging method is to take a neighborhood S for each pixel point of the original image F (x,y) containing noise and calculate the average of the gray level of all pixels in S as the pixel value of the processed image G (x,y), namely:



Median filtering is a kind of nonlinear processing technology. In fact, it is to determine a sliding window and take the median value of the window pixels as the pixel value of the processed image.

Ii. Source code

% neighborhood average method close all; clear all; clc; a=imread('lena.jpg');
subplot(231); imshow(a); title('original');
 b1=imnoise(a,'salt & pepper');
  subplot(232); imshow(b1); title('Add salt and pepper noise');
% b1=imnoise(a,'gaussian');
%  subplot(232); imshow(b1); %title('Add gaussian noise');
[m1,n1]=size(a);
c1=b1;
for i=2:m1- 1
    for j=2:n1- 1
       s=b1(i- 1:i+1,j- 1:j+1);
   
    end
end
subplot(234); imshow(c1); title('4 Neighborhood Filtering ');
c2=b1;
for i=2:m1- 1
    for j=2:n1- 1
      
    end
end
subplot(235); imshow(c2); title('8 Neighborhood Filtering ');

c3=b1;
for i=3:m1- 3
    for j=3:n1- 3
       s=b1(i2 -:i+2,j2 -:j+2);
       
    end
end
subplot(236); imshow(c3); title('12 Neighborhood Filtering '); % median filter close all; clear all; clc a=imread('lena.jpg');
subplot(221); imshow(a); title('original');
b1=imnoise(a,'salt & pepper');
%b1=imnoise(a,'gaussian'); title('Add gaussian noise');
subplot(222); imshow(b1); title('Add salt and pepper noise');
[m1,n1]=size(a);
d1=b1;
for i=2:m1- 1
    for j=2:n1- 1
       s=b1(i- 1:i+1,j- 1:j+1);
       s1=s(:);
Copy the code

3. Operation results





Fourth, note

Version: 2014 a