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//Ex5_1
// Noisy Images and their Histogram
//To plot the PDF of different Noise Distribution and add the same to the gray scale image.
//(I)Gaussian (II)Uniform (III)Salt & Pepper (IV)Log Normal (V)Rayleigh (VI)Erlang (VII)Exponetial
// Version : Scilab 5.4.1
// Operating System : Window-xp, Window-7
//Toolbox: Image Processing Design 8.3.1-1
//Toolbox: SIVP 0.5.3.1-2
//Reference book name : Digital Image Processing
//book author: Rafael C. Gonzalez and Richard E. Woods
clc;
close;
clear;
xdel(winsid())//to close all currently open figure(s).
function R=imnoise2(type,M,N,a,b)
if argn(2)==3 then
a=0; b=1;
end
select type
case'gaussian'then
rand("normal")
R=a+b*rand(M,N);
case'uniform'then
R=a+(b-a)*rand(M,N,"uniform");
case'salt & pepper'then
if argn(2)==3
a = 0.15; b = 0.15;
end
if (a+b) > 1
error('The sum Pa + Pb must not exceed 1.');
end
R(1:M,1:N) = 0.5;
X = rand(M,N);
[r c] = find(X<=a);
for i=1:length(r)
R(r(i),c(i)) = 0;
end
u = a + b;
[r c] = find(X>a & X<=u);
for i=1:length(r)
R(r(i),c(i)) = 255;
end
case'lognormal'then
if argn(2)==3
a = 1; b = 0.25;
end
R = a*exp(b*mtlb_randn(M,N));
case'rayleigh'then
if argn(2)==3
a = 1; b = 0.25;
end
R = a + ((-b)*(log(1-rand(M,N,"uniform")))).^0.5;
case'exponential'then
if argn(2)==3
a = 1;
end
if a<=0
error('Parameter a must be positive for exponential type.');
end
k = -1/a;
R = k*log(1-rand(M,N,"uniform"));
case'erlang'then
if (b ~= round(b) | b <= 0)
error('Param b must be positive for integer for Erlang.')
end
k = -1/a;
R = zeros(M,N);
for j=1:b
R = R + k*log(1-rand(M,N,"uniform"));
end
else
disp('Unknownfiltertype.')
end
endfunction
/////////////////////////////////////// Main Programm /////////////////////////////
gray=imread("Ex5_1.tif");
//gray=rgb2gray(a);
//gray=im2double(gray);
figure,ShowImage(gray,'Gray Image');
title('Original Image');
[M,N]=size(gray);
[count,cell]=imhist(gray);
figure,bar(cell,count,0.2);
mtlb_axis([0 255 0 35000]);
title('Histogram of Original Image');
/////////////////////////////////////// Gaussian Noise ////////////////////
r1=imnoise2('gaussian',M,N,15,5); // Generate Gaussian Noise with Given Mean and Variance
gray_noise_gaussian=gray+(r1);
figure,ShowImage(gray_noise_gaussian,'Gray Image with Noise');
title('Gray Image with Noise gaussian');
[count,cell]=imhist(gray_noise_gaussian);
figure;bar(cell,count,1.2);
mtlb_axis([0 255 0 3000]);
title('Gaussian');
/////////////////////////////////////// Rayleigh Noise ////////////////////
r2=imnoise2('rayleigh',M,N,0,55); // Generate rayleigh Noise
gray_noise_rayleigh=gray+(r2);
figure,ShowImage(gray_noise_rayleigh,'Gray Image with Noise');
title('Gray Image with Noise rayleigh');
[count,cell]=imhist(gray_noise_rayleigh);
figure;bar(cell,count,1.2);
mtlb_axis([0 255 0 4000]);
title('Rayleigh');
/////////////////////////////////////// Erlang (Gamma) Noise ////////////////////
r3=imnoise2('erlang',M,N,2,15); // Generate erlang Noise
gray_noise_erlang=gray+(r3);
figure,ShowImage(gray_noise_erlang,'Gray Image with Noise');
title('Gray Image with Noise erlang(Gamma)');
[count,cell]=imhist(gray_noise_erlang);
figure;bar(cell,count,1.2);
mtlb_axis([0 255 0 9500]);
title('Erlang (Gamma)');
/////////////////////////////////////// Exponential Noise ////////////////////
r4=imnoise2('exponential',M,N,0.15); //Generate exponential Noise
gray_noise_exponential=gray+(r4);
figure,ShowImage(gray_noise_exponential,'Gray Image with Noise');
title('Gray Image with Noise exponential');
[count,cell]=imhist(gray_noise_exponential);
figure;bar(cell,count,1.2);
mtlb_axis([0 255 0 4500]);
title('Exponential');
/////////////////////////////////////// Uniform Noise ////////////////////
r5=imnoise2('uniform',M,N,0,20); // Generate uniform Noise
gray_noise_uniform=gray+(r5);
figure,ShowImage(gray_noise_uniform,'Gray Image with Noise');
title('Gray Image with Noise uniform');
[count,cell]=imhist(gray_noise_uniform);
figure;bar(cell,count,1.2);
mtlb_axis([0 255 0 2000]);
title('Uniform');
/////////////////////////////////////// Salt & pepper Noise ////////////////////
r6=imnoise2('salt & pepper',M,N,0.15,0.15); // Generate salt & pepper Noise
gray_noise_salt_pepper=gray+(r6);
figure,ShowImage(gray_noise_salt_pepper,'Gray Image with Noise');
title('Gray Image with Noise salt&pepper');
[count,cell]=imhist(gray_noise_salt_pepper);
figure;bar(cell,count,1.2);
mtlb_axis([0 255 0 35000]);
title('Salt & pepper');
///////////////////////////////////////// lognormal Noise ////////////////////
//r7=imnoise2('lognormal',M,N,5,0.65); // Generate lognormal Noise
//gray_noise_lognormal=gray+(r7);
//figure,ShowImage(gray_noise_lognormal,'Gray Image with Noise');
//title('Gray Image with Noise lognormal');
//[count,cell]=imhist(gray_noise_lognormal);
//figure;bar(cell,count,1.2);
//mtlb_axis([0 255 0 5500]);
//title('lognormal');
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