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| author | priyanka | 2015-06-24 15:03:17 +0530 |
|---|---|---|
| committer | priyanka | 2015-06-24 15:03:17 +0530 |
| commit | b1f5c3f8d6671b4331cef1dcebdf63b7a43a3a2b (patch) | |
| tree | ab291cffc65280e58ac82470ba63fbcca7805165 /3176/CH5 | |
| download | Scilab-TBC-Uploads-b1f5c3f8d6671b4331cef1dcebdf63b7a43a3a2b.tar.gz Scilab-TBC-Uploads-b1f5c3f8d6671b4331cef1dcebdf63b7a43a3a2b.tar.bz2 Scilab-TBC-Uploads-b1f5c3f8d6671b4331cef1dcebdf63b7a43a3a2b.zip | |
initial commit / add all books
Diffstat (limited to '3176/CH5')
| -rw-r--r-- | 3176/CH5/EX5.1/Ex5_1.sce | 176 | ||||
| -rw-r--r-- | 3176/CH5/EX5.10/Ex5_10.sce | 44 | ||||
| -rw-r--r-- | 3176/CH5/EX5.11/Ex5_11.sce | 109 | ||||
| -rw-r--r-- | 3176/CH5/EX5.12/Ex5_12.sce | 98 | ||||
| -rw-r--r-- | 3176/CH5/EX5.2/Ex5_2.sce | 214 | ||||
| -rw-r--r-- | 3176/CH5/EX5.3/Ex5_3.sce | 216 | ||||
| -rw-r--r-- | 3176/CH5/EX5.4/Ex5_4.sce | 89 | ||||
| -rw-r--r-- | 3176/CH5/EX5.5/Ex5_5.sce | 57 | ||||
| -rw-r--r-- | 3176/CH5/EX5.8/Ex5_8.sce | 65 |
9 files changed, 1068 insertions, 0 deletions
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');
+
+
+
+
+
+
diff --git a/3176/CH5/EX5.10/Ex5_10.sce b/3176/CH5/EX5.10/Ex5_10.sce new file mode 100644 index 000000000..dddcbe872 --- /dev/null +++ b/3176/CH5/EX5.10/Ex5_10.sce @@ -0,0 +1,44 @@ +//Ex5_10
+//Image Bluring Due to Motion
+// 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).
+
+gray=imread("Ex5_10.png");
+gray=im2double(rgb2gray(gray));
+//gray=im2double(imresize(a,0.5));
+figure,ShowImage(gray,'Gray Image');
+title('Original Image','color','blue','fontsize',4);
+[M,N]=size(gray);
+
+h=fft2(gray);//fft2() is used to find 2-Dimensional Fast Fourier Transform of an matrix
+i=log(1+abs(h));
+in=fftshift(i);//fftshift() is used to rearrange the fft output, moving the zero frequency to the center of the spectrum.
+inm=mat2gray(in)
+
+a=0.1;b=0.1;T=1; // Motion and Exposure Value
+for u=1:M
+ for v=1:N
+ H(u,v)=(T/(%pi*(u*a+v*b)))*(sin(%pi*(u*a+v*b)))*exp(-%i*%pi*(u*a+v*b)); //Motion Blure Function
+ end
+end
+
+n=h.*H;//Multiply the Original Spectrum with the Degradation Function.
+Image_filter=abs(ifft(n));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Motion Blure Image','color','blue','fontsize',4);
+
+
+
+
+
+
diff --git a/3176/CH5/EX5.11/Ex5_11.sce b/3176/CH5/EX5.11/Ex5_11.sce new file mode 100644 index 000000000..a6f1901cd --- /dev/null +++ b/3176/CH5/EX5.11/Ex5_11.sce @@ -0,0 +1,109 @@ +//Ex5_11
+//Inverse Filtering
+// 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[H,H1]=lowpassfilter(type1,M,N,D0,n,k)//lowpassfilter is used to filter an image .
+ u=0:(M-1);
+ v=0:(N-1);
+ idx=find(u>M/2);
+ u(idx)=u(idx)-M;
+ idy=find(v>N/2);
+ v(idy)=v(idy)-N;
+ [U,V]=meshgrid(v,u);
+ D=sqrt(U.^2+V.^2); //Distance Calculation
+ D=fftshift(D);
+ for i=1:M
+ for j=1:N
+ H(i,j)=exp(-k.*((i-(M/2))^2+(j-(N/2))^2).^(5/6)); //Atmospheric Degradation Function
+ end
+ end
+
+ select type1
+
+ case'inverse'then
+ if argn(2)==4 then
+ n=1;k=0.0025;
+ end
+ H=H;
+ H1=H;
+
+ case'butterworth'then
+ if argn(2)==4 then
+ n=1;
+ end
+// H1 = (ones(M,N)./(1+(D./D0).^(2*n)));
+ H1=double(D<=D0);
+ H=H.*H1;
+
+ else
+ disp('Unknownfiltertype.')
+ end
+endfunction
+
+/////////////////////////////////// Main Programm ////////////////////////////////
+gray=imread('Ex5_11.png');
+gray=im2double(rgb2gray(gray));
+figure,ShowImage(gray,'Gray Image');
+title('Original Image','color','blue','fontsize',4);
+[M,N]=size(gray);
+
+h=fft2(gray);//fft2() is used to find 2-Dimensional Fast Fourier Transform of an matrix
+in=fftshift(h);//fftshift() is used to rearrange the fft output, moving the zero frequency to the center of the spectrum.
+i=log(1+abs(in));
+
+inm=mat2gray(i)
+
+////////////////////////// Filtering With Cut-off Frequency 480 ///////////////////////
+[filt,H1]=lowpassfilter('inverse',M,N,480,1,0.0025); // Function which generate Filter Mask Corresponding to Low Frequency
+//filt_shift=fftshift(filt);
+//figure,ShowImage(abs(filt),'Filter Mask');
+//title('Filter Mask to Specific Cut-Off Frequency');
+n=in./(filt+%eps);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=abs(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Full Inverse Filter','color','blue','fontsize',4);
+
+////////////////////////// Filtering With Cut-off Frequency 40 ///////////////////////
+[filt,H1]=lowpassfilter('butterworth',M,N,40,10,0.0025); // Function which generate Filter Mask Corresponding to Low Frequency
+//filt_shift=fftshift(filt);
+//figure,ShowImage(abs(filt),'Filter Mask');
+//title('Filter Mask to Specific Cut-Off Frequency');
+n=(in.*H1)./(filt+%eps);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=abs(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Cut-Off Frequency 40','color','blue','fontsize',4);
+
+////////////////////////// Filtering With Cut-off Frequency 70 ///////////////////////
+[filt,H1]=lowpassfilter('butterworth',M,N,70,10,0.0025); // Function which generate Filter Mask Corresponding to Low Frequency
+//filt_shift=fftshift(filt);
+//figure,ShowImage(abs(filt),'Filter Mask');
+//title('Filter Mask to Specific Cut-Off Frequency');
+n=(in.*H1)./(filt+%eps);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=abs(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Cut-Off Frequency 70','color','blue','fontsize',4);
+
+////////////////////////// Filtering With Cut-off Frequency 100 ///////////////////////
+[filt,H1]=lowpassfilter('butterworth',M,N,100,10,0.0025); // Function which generate Filter Mask Corresponding to Low Frequency
+//filt_shift=fftshift(filt);
+//figure,ShowImage(abs(filt),'Filter Mask');
+//title('Filter Mask to Specific Cut-Off Frequency');
+n=(in.*H1)./(filt+%eps);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=abs(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Cut-Off Frequency 100','color','blue','fontsize',4);
+
diff --git a/3176/CH5/EX5.12/Ex5_12.sce b/3176/CH5/EX5.12/Ex5_12.sce new file mode 100644 index 000000000..0ef07a070 --- /dev/null +++ b/3176/CH5/EX5.12/Ex5_12.sce @@ -0,0 +1,98 @@ +//Ex5_12
+//Comparision of Inverse Filtering and Wiener Filtering
+// 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[H,H1]=lowpassfilter(type1,M,N,D0,n,k)//lowpassfilter is used to filter an image .
+ u=0:(M-1);
+ v=0:(N-1);
+ idx=find(u>M/2);
+ u(idx)=u(idx)-M;
+ idy=find(v>N/2);
+ v(idy)=v(idy)-N;
+ [U,V]=meshgrid(v,u);
+ D=sqrt(U.^2+V.^2); //Distance Calculation
+ D=fftshift(D);
+ for i=1:M
+ for j=1:N
+ H(i,j)=exp(-k.*((i-(M/2))^2+(j-(N/2))^2).^(5/6)); //Atmospheric Degradation Function
+ end
+ end
+
+ select type1
+
+ case'inverse'then
+ if argn(2)==4 then
+ n=1;k=0.0025;
+ end
+ H=H;
+ H1=H;
+
+ case'butterworth'then
+ if argn(2)==4 then
+ n=1;
+ end
+// H1 = (ones(M,N)./(1+(D./D0).^(2*n)));
+ H1=double(D<=D0);
+ H=H.*H1;
+
+ else
+ disp('Unknownfiltertype.')
+ end
+endfunction
+
+/////////////////////////////////// Main Programm ////////////////////////////////
+gray=imread('Ex5_12.png');
+gray=im2double(rgb2gray(gray));
+figure,ShowImage(gray,'Gray Image');
+title('Original Image','color','blue','fontsize',4);
+[M,N]=size(gray);
+
+h=fft2(gray);//fft2() is used to find 2-Dimensional Fast Fourier Transform of an matrix
+in=fftshift(h);//fftshift() is used to rearrange the fft output, moving the zero frequency to the center of the spectrum.
+i=log(1+abs(in));
+
+inm=mat2gray(i)
+
+////////////////////////// Filtering With Cut-off Frequency 480 ///////////////////////
+[filt,H1]=lowpassfilter('inverse',M,N,480,1,0.0025); // Function which generate Filter Mask Corresponding to Low Frequency
+//filt_shift=fftshift(filt);
+//figure,ShowImage(abs(filt),'Filter Mask');
+//title('Filter Mask to Specific Cut-Off Frequency');
+n=in./(filt+%eps);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=abs(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Full Inverse Filter','color','blue','fontsize',4);
+
+////////////////////////// Filtering With Cut-off Frequency 40 ///////////////////////
+[filt,H1]=lowpassfilter('butterworth',M,N,40,10,0.0025); // Function which generate Filter Mask Corresponding to Low Frequency
+//filt_shift=fftshift(filt);
+//figure,ShowImage(abs(filt),'Filter Mask');
+//title('Filter Mask to Specific Cut-Off Frequency');
+n=(in.*H1)./(filt+%eps);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=abs(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Cut-Off Frequency 40','color','blue','fontsize',4);
+
+
+////////////////////////// Filtering With Cut-off Frequency 40 ///////////////////////
+[filt,H1]=lowpassfilter('butterworth',M,N,40,10,0.0025); // Function which generate Filter Mask Corresponding to Low Frequency
+//filt_shift=fftshift(filt);
+//figure,ShowImage(abs(filt),'Filter Mask');
+//title('Filter Mask to Specific Cut-Off Frequency');
+n=(in.*H1)./(((1/filt+%eps).*(filt^2/(filt^2+6)))+%eps); // Wiener Filtering.
+Image_filter=abs(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Cut-Off Frequency 40','color','blue','fontsize',4);
diff --git a/3176/CH5/EX5.2/Ex5_2.sce b/3176/CH5/EX5.2/Ex5_2.sce new file mode 100644 index 000000000..607616bb8 --- /dev/null +++ b/3176/CH5/EX5.2/Ex5_2.sce @@ -0,0 +1,214 @@ +//Ex5_2
+// Illustration of Mean Filters
+//To impliment the Following Mean Restoration filter
+// (I)Arithmetic (II)Geometric (III)Harmonic (IV)Contra Harmonic
+
+// 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]=geometric_mean2(g,m,n);//gmean2() is used to filter an image using Geometric mean filter
+ size1=m;
+ q=m*n;
+ [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)) ;
+ temp2(i,j)=geomean(t);
+ end
+ end
+ nn=temp2(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)
+ f=mat2gray(nn)
+endfunction
+
+function [f]=Harmonic_mean(g,m,n) //harmean1() is used to filter an image using Harmonic mean filter.
+ size1=m;
+ d=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:$,1:$);
+
+ 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)) ;
+ t1=ones(m,n)./(t+%eps);
+ t2=sum(t1);
+ temp2(i,j)=d/t2;
+ end
+ end
+ nn=temp2(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1);
+ f=mat2gray(nn);
+endfunction
+
+function [f]=Contra_Harmonic_mean(g,m,n,Q) //charmean1() is use to filter an image using Contra Harmonic mean filter
+ size1=m;
+ d=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:$,1:$)
+ disp(Q)
+ 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)) ;
+ d1=(t+%eps).^Q;
+ n1=(t+%eps).^(Q+1);
+ d2=sum(d1);
+ n2=sum(n1);
+ temp2(i,j)=n2/(d2);
+ end
+ end
+ nn=temp2(ceil(size1/2):nr+ceil(size1/2)-1,ceil(size1/2):nc+ceil(size1/2)-1)
+ f=nn;
+endfunction
+
+///////////////////////////////////// Main Programm ////////////////////
+
+gray=imread("Ex5_2.tif");
+//gray=rgb2gray(a);
+//gray=im2double(gray);
+figure,ShowImage(gray,'Gray Image');
+title('Original Image');
+[M,N]=size(gray);
+
+///////////////////////////////////// Arithmetical Mean Filter ////////////////////
+v=imnoise(gray,'gaussian',0,0.02);
+figure,ShowImage(v,'Noisy Image');
+title('Image with Gaussian Noise');
+m=3;n=3;
+[f]=arithmetic_mean(v,m,n);
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Arithmetical Mean Filter');
+
+///////////////////////////////////// Geometric Mean Filter ////////////////////
+v=imnoise(gray,'gaussian',0,0.02);
+figure,ShowImage(v,'Noisy Image');
+title('Image with Gaussian Noise');
+m=3;n=3;
+[f]=geometric_mean1(v,m,n);
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Geometric Mean Filter');
+
+
+/////////////////////////////////////// Geometric Mean Filter ////////////////////
+//v=imnoise(gray,'gaussian',0,0.02);
+//figure,ShowImage(v,'Noisy Image');
+//title('Image with Gaussian Noise');
+//m=3;n=3;
+//[f]=geometric_mean2(v,m,n);
+//figure,ShowImage(f,'Recovered Image');
+//title('Recovered Image with Geometric Mean Filter');
+
+
+/////////////////////////////////////// Harmonic Mean 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('Image with Salt Noise');
+//m=3;n=3;
+//[f]=Harmonic_mean(gray_noise_salt,m,n);
+//figure,ShowImage(f,'Recovered Image');
+//title('Recovered Image with Harmonic Mean Filter');
+//
+//////////////////////////////// Contra_Harmonic Mean Filter (Pepper) ////////////////////
+temp(1:M,1:N)=0.5;
+r3=imnoise(temp,'salt & pepper',0.05); //Generate salt & pepper Noise
+gray_noise_pepper=gray; //Add pepper Noise Only
+[r c]=find(r3==0); //Find pepper Noise Only
+ for i=1:length(r)
+ gray_noise_pepper(r(i),c(i)) = 0;
+ end
+figure,ShowImage(gray_noise_pepper,'Noisy Image');
+title('Image with pepper Noise');
+m=3;n=3;Q=1.5;
+[f]=Contra_Harmonic_mean(gray_noise_pepper,m,n,Q);
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Contra Harmonic Mean Filter[ Q=1.5 ]');
+
+/////////////////////////////////// Contra_Harmonic Mean Filter (Salt) ////////////////////
+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('Image with Salt Noise');
+m=3;n=3;Q=-1.5;
+[f]=Contra_Harmonic_mean(gray_noise_salt,m,n,Q);
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Contra Harmonic Mean Filter[ Q=-1.5 ]');
+
+
+////////////////////////////// Contra_Harmonic Mean Filter (Pepper) ////////////////////
+temp(1:M,1:N)=0.5;
+r3=imnoise(temp,'salt & pepper',0.05); //Generate salt & pepper Noise
+gray_noise_pepper=gray; // Add pepper Noise Only
+[r c]=find(r3==0); //Find pepper Noise Only
+ for i=1:length(r)
+ gray_noise_pepper(r(i),c(i)) = 0;
+ end
+figure,ShowImage(gray_noise_pepper,'Noisy Image');
+title('Image with pepper Noise');
+m=3;n=3;Q=-1.5;
+[f]=Contra_Harmonic_mean(gray_noise_pepper,m,n,Q);
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Contra Harmonic Mean Filter[ Q=-1.5 ]');
+
+/////////////////////////////////// Contra_Harmonic Mean Filter (Salt) ////////////////////
+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('Image with Salt Noise');
+m=3;n=3;Q=1.5;
+[f]=Contra_Harmonic_mean(gray_noise_salt,m,n,Q);
+figure,ShowImage(f,'Recovered Image');
+title('Recovered Image with Contra Harmonic Mean Filter[ Q=1.5 ]');
diff --git a/3176/CH5/EX5.3/Ex5_3.sce b/3176/CH5/EX5.3/Ex5_3.sce new file mode 100644 index 000000000..6d8c27f00 --- /dev/null +++ b/3176/CH5/EX5.3/Ex5_3.sce @@ -0,0 +1,216 @@ +//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');
+
diff --git a/3176/CH5/EX5.4/Ex5_4.sce b/3176/CH5/EX5.4/Ex5_4.sce new file mode 100644 index 000000000..ca1fde8d4 --- /dev/null +++ b/3176/CH5/EX5.4/Ex5_4.sce @@ -0,0 +1,89 @@ +//Ex5_4
+//Illustration of Adaptive Local Noise Reduction Filtering
+// 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;
+clear;
+close;
+xdel(winsid());
+
+///////////////// Function File /////////////////////
+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
+
+
+///////////////// Main Programm /////////////////////
+A=imread("Ex5_4.tif");
+B = imnoise(A,'gaussian',0,0.01);
+[rw1 ,cl1]=size(B);
+figure;
+ShowImage(B,'Gaussian noise added');
+title('Image with gaussian noise','color','blue','fontsize',4);
+
+///////////////////////////////////// Arithmetical Mean Filter ////////////////////
+m=7;n=7;
+[f]=arithmetic_mean(B,m,n);
+figure,ShowImage(f,'Recovered Image');
+title('Restored Image with Arithmetical Mean Filter','color','blue','fontsize',4);
+
+///////////////////////////////////// Geometric Mean Filter ////////////////////
+m=7;n=7;
+[f]=geometric_mean1(B,m,n);
+figure,ShowImage(f,'Recovered Image');
+title('Restored Image with Geometric Mean Filter','color','blue','fontsize',4);
+
+
+
+////////////////////Adaptive Local Noise Reduction///////////////////////
+B= double(B);
+M=7;
+N=7;
+lvar=zeros([rw1-M+1,cl1-N+1]);
+lmean=zeros([rw1-M+1,cl1-N+1]);
+temp=zeros([rw1-M+1,cl1-N+1]);
+F=zeros([rw1-M+1,cl1-N+1]);
+sz=(rw1-M+1)*(cl1-N+1);
+for i=1:rw1-M+1
+ for j=1:cl1-N+1
+ temp=B(i:i+(M-1),j:j+(N-1));
+ lmean(i,j)=mean(temp);
+ lvar(i,j)=mean(temp.*temp)-mean(temp).^2;
+ end
+end
+nvar=sum(lvar)/sz;
+lvar=max(lvar,nvar);
+C=B(M/2:rw1-M/2,N/2:cl1-N/2);
+F=nvar./lvar;
+F=F.*(C-lmean);
+F=C-F;
+F=uint8(F);
+figure;
+ShowImage(F,'Restored');
+title('Restored Image using Adaptive Local filter','color','blue','fontsize',4);
diff --git a/3176/CH5/EX5.5/Ex5_5.sce b/3176/CH5/EX5.5/Ex5_5.sce new file mode 100644 index 000000000..f29163642 --- /dev/null +++ b/3176/CH5/EX5.5/Ex5_5.sce @@ -0,0 +1,57 @@ +//Ex5_5
+// Illustration of Adaptive Median Filter
+// 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;
+clear;
+close;
+xdel(winsid());
+A=imread("Ex5_5.tif");
+A=imresize(A,[256 256]);
+A=imnoise(A,'salt & pepper',0.25); // Add Sali & Pepper Noise
+figure,ShowImage(A,'Salt & pepper Image');
+title('Image with Salt & pepper noise (Density = 0.25)','color','blue','fontsize',4);
+figure,ShowImage(MedianFilter(A,[7 7]),'Median filter with mask 7x7');
+title('Restored Image using Median filter with 7*7 Mask','color','blue','fontsize',4);
+
+///////////////// Adaptive Median Filter /////////////////////////
+[r c]=size(A);
+n=7 // Maximum Window size
+a=(n-1)/2;
+C=zeros(r-2*a,c-2*a);
+for i=a+1:(r-a)
+ for j=a+1:(c-a)
+ for b=3:2:7
+ d=(b-1)/2
+ x=A(i,j);
+ p=imcrop(A,[i-d j-d b b]) // Crop the Sub Image form Original Iamge
+ med=median(p); // To Find Median Value
+ maxx=max(p); // To Find Max Value
+ minn=min(p); // To Find Min Value
+ if (med>minn & med<maxx) then
+ if(x>minn & x<minn) then
+ C(j-a+1,i-a+1)=x;
+ clear p;
+ break;
+ else
+ C(j-a+1,i-a+1)=med;
+ clear p;
+ break;
+ end
+ elseif b<7 then
+ continue;
+ else
+ C(j-a+1,i-a+1)=med;
+ clear p;
+ break;
+ end
+ end
+end
+end
+figure;ShowImage(C,'Adaptive Median filter Image using code');
+title('Restored Image using Adaptive Median filter','color','blue','fontsize',4);
diff --git a/3176/CH5/EX5.8/Ex5_8.sce b/3176/CH5/EX5.8/Ex5_8.sce new file mode 100644 index 000000000..db4e987db --- /dev/null +++ b/3176/CH5/EX5.8/Ex5_8.sce @@ -0,0 +1,65 @@ +//Ex5_8
+//Removal of Periodic Noise by Notch Filtering
+// 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[H]=notchfilter(M,N,W)//notchfilter is used to filter an image .
+ H=ones(M,N);
+ H(1:ceil(M/2-10),ceil(N/2-W/2):ceil(N/2+W/2))=0;
+ H(ceil(M/2+10):M,ceil(N/2-W/2):ceil(N/2+W/2))=0;
+
+endfunction
+
+
+
+/////////////////////////////////// Main Programm ////////////////////////////////
+a=imread("Ex5_8.tif");
+//gray=rgb2gray(a);
+gray=im2double(imresize(a,0.5));
+figure,ShowImage(gray,'Gray Image');
+title('Original Image');
+[M,N]=size(gray);
+
+h=fft2(gray);//fft2() is used to find 2-Dimensional Fast Fourier Transform of an matrix
+i=log(1+abs(h));
+in=fftshift(i);//fftshift() is used to rearrange the fft output, moving the zero frequency to the center of the spectrum.
+inm=mat2gray(in)
+figure,ShowImage(inm,'Frequency Spectrum');
+title('Frequency Spectrum');
+
+filt=notchfilter(M,N,3); // User Define Function which generate Filter Mask Corresponding to Low Frequency
+filt_pass=1-filt;
+//filt_shift=fftshift(filt);
+figure,ShowImage(filt,'Filter Mask');
+title('Filter Mask (Band stop) to Specific Cut-Off Frequency');
+
+n=filt.*fftshift(h);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=real(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image with Specific Cut-Off Frequency');
+
+
+figure,ShowImage(filt_pass,'Filter Mask');
+title('Filter Mask (Band Pass) to Specific Cut-Off Frequency');
+
+n=filt_pass.*fftshift(h);//Multiply the Original Spectrum with the Filter Mask.
+Image_filter=real(ifft(fftshift(n)));
+Image_filter=mat2gray(Image_filter)
+figure,ShowImage(Image_filter,'Filtered Image');
+title('Filtered Image (Noise Pattern) with Specific Cut-Off Frequency');
+
+
+
+
+
+
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