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diff --git a/3176/CH5/EX5.1/Ex5_1.sce b/3176/CH5/EX5.1/Ex5_1.sce new file mode 100644 index 000000000..bb5e70081 --- /dev/null +++ b/3176/CH5/EX5.1/Ex5_1.sce @@ -0,0 +1,176 @@ +//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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