Gaussian filter study matlab codes

This program show the effect of Gaussian filter. The output are four subfigures shown in the same figure:

• Subfigure 1: The initial noise free "lena"
• Subfigure 2: The noisy "lena"
• Subfigure 3: Filtered the initial "lena"
• Subfigure 4: Filtered the noisy "lena"

  The matlab codes:
  %%%%%%%%%%%%% The main.m file  %%%%%%%%%%%%%%%
  clear;
  % Parameters of the Gaussian filter:
  n1=10;sigma1=3;n2=10;sigma2=3;theta1=0;
  % The amplitude of the noise:
  noise=0.1;
  
  [w,map]=gifread('lena.gif');               
  x=ind2gray(w,map);
  filter1=d2gauss(n1,sigma1,n2,sigma2,theta);
  x_rand=noise*randn(size(x));
  y=x+x_rand;
  f1=conv2(x,filter1,'same');
  rf1=conv2(y,filter1,'same');
  figure(1);
  subplot(2,2,1);imagesc(x);
  subplot(2,2,2);imagesc(y);
  subplot(2,2,3);imagesc(f1);
  subplot(2,2,4);imagesc(rf1);
  colormap(gray);
  %%%%%%%%%%%%%% End of the main.m file %%%%%%%%%%%%%%%
  
  
  %%%%%%% The functions used in the main.m file %%%%%%%
  % Function "d2gauss.m":
  % This function returns a 2D Gaussian filter with size n1*n2; theta is 
  % the angle that the filter rotated counter clockwise; and sigma1 and sigma2
  % are the standard deviation of the gaussian functions.
  function h = d2gauss(n1,std1,n2,std2,theta)
  r=[cos(theta) -sin(theta);
     sin(theta)  cos(theta)];
  for i = 1 : n2 
      for j = 1 : n1
          u = r * [j-(n1+1)/2 i-(n2+1)/2]';
          h(i,j) = gauss(u(1),std1)*gauss(u(2),std2);
      end
  end
  h = h / sqrt(sum(sum(h.*h)));
  
  % Function "gauss.m":
  function y = gauss(x,std)
  y = exp(-x^2/(2*std^2)) / (std*sqrt(2*pi));
  %%%%%%%%%%%%%% end of the functions %%%%%%%%%%%%%%%%
  

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