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-//Ex5_3
-// Illustration of Order Statistic filter
-//To impliment the Following Order Statistic Restoration filter
-// (I)Median (II)MAX (III)MIN (IV)Mid Point (V)Alpha trimmed.
-
-// 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 [f]=arithmetic_mean(v,m,n)
- w=fspecial('average',m);
- f=imfilter(v,w);
-endfunction
-
-function [f]=geometric_mean1(g,m,n);//gmean1() is used to filter an image using Geometric mean filter
- size1=m;
- q=m*n;
- g=double(g);
- [nr,nc]=size(g);
- temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
- temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=g(1:$,1:$)
- temp=temp+1;
- for i=ceil(size1/2):nr+ceil(size1/2)-1
- for j=ceil(size1/2):nc+ceil(size1/2)-1
- t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
- temp2(i,j)=prod(t);
- end
- end
- temp3=temp2.^(1/q);
- nn=temp3(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)
- f1=nn-1;
- f=mat2gray(f1)
-endfunction
-
-function [f]=restoration_filter(v,type,m,n,Q,d)
- if argn(2) ==2 then
- m=7;n=7;Q=1.5;d=10;
- elseif argn(2)==5 then
- Q=parameter;d=parameter;
- elseif argn(2)==4 then
- Q=1.5;d=2;
- else
- disp('wrong number of inputs');
- end
-
- select type
-
- case'median'then
- f=MedianFilter(v,[m n]);
-
- case'MIN'then
- size1=m;
- [nr,nc]=size(v);
- temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
- temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=v(1:$,1:$);
- for i=ceil(size1/2):nr+ceil(size1/2)-1
- for j=ceil(size1/2):nc+ceil(size1/2)-1
- t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
- y=gsort(t);
- temp2(i-floor(size1/2),j-floor(size1/2))=min(y);
- end
- end
- f=mat2gray(temp2);
-
- case'MAX'then
- size1=m;
- [nr,nc]=size(v);
- temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
- temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=v(1:$,1:$);
- for i=ceil(size1/2):nr+ceil(size1/2)-1
- for j=ceil(size1/2):nc+ceil(size1/2)-1
- t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
- y=gsort(t);
- temp2(i-floor(size1/2),j-floor(size1/2))=max(y);
- end
- end
- f=mat2gray(temp2);
-
- case'Mid_Point'then
- size1=m;
- [nr,nc]=size(v);
- temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
- temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=v(1:$,1:$);
- for i=ceil(size1/2):nr+ceil(size1/2)-1
- for j=ceil(size1/2):nc+ceil(size1/2)-1
- t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
- y=gsort(t);
- temp2(i-floor(size1/2),j-floor(size1/2))=0.5*(min(y)+max(y));
- end
- end
- f=mat2gray(temp2);
-
- else
- disp('Unknownfiltertype.')
- end
-endfunction
-
-function [f]=alphatrim(g,m,n,d)//alphatrim()is used to filter an image using alpha-trimmed mean filter
- size1=m;
- [nr,nc]=size(g);
- temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
- temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=g(1:$,1:$)
-
- for i=ceil(size1/2):nr+ceil(size1/2)-1
- for j=ceil(size1/2):nc+ceil(size1/2)-1
- t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2))
- y=gsort(t);
- a=y(:)
- b=a';
- t1=b(1+d/2:$-d/2);
- temp2(i-floor(size1/2),j-floor(size1/2))=mean(t1);
- end
- end
- f=mat2gray(temp2)
-endfunction
-
-
-///////////////////////////////////// Main Programm ////////////////////
-
-gray=imread("Ex5_3.tif");
-//gray=rgb2gray(a);
-//gray=im2double(gray);
-figure,ShowImage(gray,'Gray Image');
-title('Original Image');
-[M,N]=size(gray);
-
-/////////////////////////////////// Median Filter ////////////////////
-v=imnoise(gray,'salt & pepper',0.1);
-figure,ShowImage(v,'Noisy Image');
-title('Original Image with Salt & Pepper Noise');
-//Filtering the corrupted image with median filter
-h=restoration_filter(v,'median',3,3);
-figure,ShowImage(h,'Recovered Image');
-title('Recovered Image with Median Filter');
-//Filtering the corrupted image with median filter
-h1=restoration_filter(h,'median',3,3);
-figure,ShowImage(h1,'Recovered Image');
-title('Recovered Image with Median Filter');
-//Filtering the corrupted image with median filter
-h2=restoration_filter(h1,'median',3,3);
-figure,ShowImage(h2,'Recovered Image');
-title('Recovered Image with Median Filter');
-
-
-/////////////////////////////////// MAX Filter ////////////////////
-temp(1:M,1:N)=0.5;
-r3=imnoise(temp,'salt & pepper',0.1); // Generate salt & pepper Noise
-gray_noise_pepper=gray; // Add Pepper Noise Only
-[r c]=find(r3==0);
- for i=1:length(r)
- gray_noise_pepper(r(i),c(i)) = 0;
- end
-figure,ShowImage(gray_noise_pepper,'Noisy Image');
-title('Noisy Image with Pepper Noise');
-
-//Filtering the Salt Noise corrupted image with MAX filter
-h=restoration_filter(gray_noise_pepper,'MAX',3,3);
-figure,ShowImage(h,'Recovered Image');
-title('Recovered Image with MAX Filter');
-
-
-//////////////////////////////////// MIN Filter ////////////////////
-temp(1:M,1:N)=0.5;
-r3=imnoise(temp,'salt & pepper',0.1); // Generate salt & pepper Noise
-gray_noise_salt=gray; // Add salt Noise Only
-[r c]=find(r3==1);
- for i=1:length(r)
- gray_noise_salt(r(i),c(i)) = 255;
- end
-figure,ShowImage(gray_noise_salt,'Noisy Image');
-title('Noisy Image');
-
-//Filtering the Salt Noise corrupted image with MIN filter
-h=restoration_filter(gray_noise_salt,'MIN',3,3);
-figure,ShowImage(h,'Recovered Image');
-title('Recovered Image with MIN Filter');
-
-
-///////////////////////////////////// Mid-Point Filter ////////////////////
-//v=imnoise(gray,'gaussian',0,0.02);
-//figure,ShowImage(v,'Noisy Image');
-//title('Image with Gaussian Noise');
-////Filtering the Salt Noise corrupted image with Mid-Point filter
-//h=restoration_filter(v,'Mid_Point',3,3);
-//figure,ShowImage(h,'Recovered Image');
-//title('Recovered Image with Mid_Point Filter');
-
-
-///////////////////////////////// Alpha Trimmed Filter ////////////////////
-v=imnoise(gray,'gaussian',0,0.02);
-v=imnoise(v,'salt & pepper',0.05);
-figure,ShowImage(v,'Noisy Image');
-title('Image with Gaussian and Salt&Pepper Noise');
-m=5;n=5;d=5;
-[f]=arithmetic_mean(v,m,n); // Filtering with Arithmetical mean
-figure,ShowImage(f,'Recovered Image');
-title('Recovered Image with Arithmetical Mean Filter');
-[f]=geometric_mean1(v,m,n); // Filtering with Geometric mean
-figure,ShowImage(f,'Recovered Image');
-title('Recovered Image with Geometric Mean Filter');
-//Filtering the corrupted image with median filter
-h=restoration_filter(v,'median',5,5); // Filtering with median Filtering
-figure,ShowImage(h,'Recovered Image');
-title('Recovered Image with Median Filter');
-f=alphatrim(v,m,n,d); // Filtering with alphatrim Filtering
-figure,ShowImage(f,'Recovered Image');
-title('Recovered Image with Alpha Trimmed Filter');
-
+//Ex5_3
+// Illustration of Order Statistic filter
+//To impliment the Following Order Statistic Restoration filter
+// (I)Median (II)MAX (III)MIN (IV)Mid Point (V)Alpha trimmed.
+
+// 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 [f]=arithmetic_mean(v,m,n)
+ w=fspecial('average',m);
+ f=imfilter(v,w);
+endfunction
+
+function [f]=geometric_mean1(g,m,n);//gmean1() is used to filter an image using Geometric mean filter
+ size1=m;
+ q=m*n;
+ g=double(g);
+ [nr,nc]=size(g);
+ temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
+ temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=g(1:$,1:$)
+ temp=temp+1;
+ for i=ceil(size1/2):nr+ceil(size1/2)-1
+ for j=ceil(size1/2):nc+ceil(size1/2)-1
+ t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
+ temp2(i,j)=prod(t);
+ end
+ end
+ temp3=temp2.^(1/q);
+ nn=temp3(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)
+ f1=nn-1;
+ f=mat2gray(f1)
+endfunction
+
+function [f]=restoration_filter(v,type,m,n,Q,d)
+ if argn(2) ==2
+ m=7;n=7;Q=1.5;d=10;
+ elseif argn(2)==5
+ Q=parameter;d=parameter;
+ elseif argn(2)==4
+ Q=1.5;d=2;
+ else
+ disp('wrong number of inputs');
+ end
+
+ select type
+
+ case'median'
+ f=MedianFilter(v,[m n]);
+
+ case'MIN'
+ size1=m;
+ [nr,nc]=size(v);
+ temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
+ temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=v(1:$,1:$);
+ for i=ceil(size1/2):nr+ceil(size1/2)-1
+ for j=ceil(size1/2):nc+ceil(size1/2)-1
+ t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
+ y=gsort(t);
+ temp2(i-floor(size1/2),j-floor(size1/2))=min(y);
+ end
+ end
+ f=mat2gray(temp2);
+
+ case'MAX'
+ size1=m;
+ [nr,nc]=size(v);
+ temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
+ temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=v(1:$,1:$);
+ for i=ceil(size1/2):nr+ceil(size1/2)-1
+ for j=ceil(size1/2):nc+ceil(size1/2)-1
+ t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
+ y=gsort(t);
+ temp2(i-floor(size1/2),j-floor(size1/2))=max(y);
+ end
+ end
+ f=mat2gray(temp2);
+
+ case'Mid_Point'
+ size1=m;
+ [nr,nc]=size(v);
+ temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
+ temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=v(1:$,1:$);
+ for i=ceil(size1/2):nr+ceil(size1/2)-1
+ for j=ceil(size1/2):nc+ceil(size1/2)-1
+ t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2)) ;
+ y=gsort(t);
+ temp2(i-floor(size1/2),j-floor(size1/2))=0.5*(min(y)+max(y));
+ end
+ end
+ f=mat2gray(temp2);
+
+ else
+ disp('Unknownfiltertype.')
+ end
+endfunction
+
+function [f]=alphatrim(g,m,n,d)//alphatrim()is used to filter an image using alpha-trimmed mean filter
+ size1=m;
+ [nr,nc]=size(g);
+ temp=zeros(nr+2*floor(size1/2),nc+2*floor(size1/2));
+ temp(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)=g(1:$,1:$)
+
+ for i=ceil(size1/2):nr+ceil(size1/2)-1
+ for j=ceil(size1/2):nc+ceil(size1/2)-1
+ t=temp(i-floor(size1/2):1:i+floor(size1/2),j-floor(size1/2):1:j+floor(size1/2))
+ y=gsort(t);
+ a=y(:)
+ b=a';
+ t1=b(1+d/2:$-d/2);
+ temp2(i-floor(size1/2),j-floor(size1/2))=mean(t1);
+ end
+ end
+ f=mat2gray(temp2)
+endfunction
+
+
+///////////////////////////////////// Main Programm ////////////////////
+
+gray=imread("Ex5_3.tif");
+//gray=rgb2gray(a);
+//gray=im2double(gray);
+figure,ShowImage(gray,'Gray Image');
+title('Original Image');
+[M,N]=size(gray);
+
+/////////////////////////////////// Median Filter ////////////////////
+v=imnoise(gray,'salt & pepper',0.1);
+figure,ShowImage(v,'Noisy Image');
+title('Original Image with Salt & Pepper Noise');
+//Filtering the corrupted image with median filter
+h=restoration_filter(v,'median',3,3);
+figure,ShowImage(h,'Recovered Image');
+title('Recovered Image with Median Filter');
+//Filtering the corrupted image with median filter
+h1=restoration_filter(h,'median',3,3);
+figure,ShowImage(h1,'Recovered Image');
+title('Recovered Image with Median Filter');
+//Filtering the corrupted image with median filter
+h2=restoration_filter(h1,'median',3,3);
+figure,ShowImage(h2,'Recovered Image');
+title('Recovered Image with Median Filter');
+
+
+/////////////////////////////////// MAX Filter ////////////////////
+temp(1:M,1:N)=0.5;
+r3=imnoise(temp,'salt & pepper',0.1); // Generate salt & pepper Noise
+gray_noise_pepper=gray; // Add Pepper Noise Only
+[r c]=find(r3==0);
+ for i=1:length(r)
+ gray_noise_pepper(r(i),c(i)) = 0;
+ end
+figure,ShowImage(gray_noise_pepper,'Noisy Image');
+title('Noisy Image with Pepper Noise');
+
+//Filtering the Salt Noise corrupted image with MAX filter
+h=restoration_filter(gray_noise_pepper,'MAX',3,3);
+figure,ShowImage(h,'Recovered Image');
+title('Recovered Image with MAX Filter');
+
+
+//////////////////////////////////// MIN Filter ////////////////////
+temp(1:M,1:N)=0.5;
+r3=imnoise(temp,'salt & pepper',0.1); // Generate salt & pepper Noise
+gray_noise_salt=gray; // Add salt Noise Only
+[r c]=find(r3==1);
+ for i=1:length(r)
+ gray_noise_salt(r(i),c(i)) = 255;
+ end
+figure,ShowImage(gray_noise_salt,'Noisy Image');
+title('Noisy Image');
+
+//Filtering the Salt Noise corrupted image with MIN filter
+h=restoration_filter(gray_noise_salt,'MIN',3,3);
+figure,ShowImage(h,'Recovered Image');
+title('Recovered Image with MIN Filter');
+
+
+///////////////////////////////////// Mid-Point Filter ////////////////////
+//v=imnoise(gray,'gaussian',0,0.02);
+//figure,ShowImage(v,'Noisy Image');
+//title('Image with Gaussian Noise');
+////Filtering the Salt Noise corrupted image with Mid-Point filter
+//h=restoration_filter(v,'Mid_Point',3,3);
+//figure,ShowImage(h,'Recovered Image');
+//title('Recovered Image with Mid_Point Filter');
+
+
+///////////////////////////////// Alpha Trimmed Filter ////////////////////
+v=imnoise(gray,'gaussian',0,0.02);
+v=imnoise(v,'salt & pepper',0.05);
+figure,ShowImage(v,'Noisy Image');
+title('Image with Gaussian and Salt&Pepper Noise');
+m=5;n=5;d=5;
+[f]=arithmetic_mean(v,m,n); // Filtering with Arithmetical mean
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Arithmetical Mean Filter');
+[f]=geometric_mean1(v,m,n); // Filtering with Geometric mean
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Geometric Mean Filter');
+//Filtering the corrupted image with median filter
+h=restoration_filter(v,'median',5,5); // Filtering with median Filtering
+figure,ShowImage(h,'Recovered Image');
+title('Recovered Image with Median Filter');
+f=alphatrim(v,m,n,d); // Filtering with alphatrim Filtering
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Alpha Trimmed Filter'); \ No newline at end of file
generated by cgit (git 2.34.1) at 2025-02-04 08:32:02 +0000