% freq.m
% demonstrating 2D filtering effects
% copyright(c) 1997 by Yu Hen Hu
% created: 10/14/97
%
clear all,clf
echo on
%
% 1. load a gray scale image
[xi,ind]=bmpread('p64.bmp');
[nh,nv]=size(xi);  % size of the image
figure(1),imshow(xi,ind),title('original image')
x=ind2gray(xi,ind);
%
% 2. spatial low pass filtering using different sizes masks
ns=input('input low pass filter mask size (odd number): ');
% 
% The mask is:
B=ones(ns,ns)/(ns*ns)
% Now filter the image using 2d fitlering command "filter2"
y=filter2(B,x);
figure(2),imagesc(y),colormap('gray'),title('low passed image')
% Note that (1) the boundary smears; (2) there are layer edges
pause
% 3. spatial high pass filtering using default masks (figure 4.24)
% 
% The mask is:
B=[-1 -1 -1;-1 8 -1;-1 -1 -1]/9;
% Now filter the image using 2d fitlering command "filter2"
y=filter2(B,x);
figure(2),imagesc(y),colormap('gray'),title('high passed image')
% Note that (1) the boundary smears; (2) there are layer edges
pause
% 4. spatial high boost filtering using selected weigting (figure 4.26)
% 
A=input('input the value of A (>1) :');
% The mask is:
B=[-1 -1 -1;-1 9*A-1 -1;-1 -1 -1]/9;
% Now filter the image using 2d fitlering command "filter2"
y=filter2(B,x);
figure(2),imagesc(y),colormap('gray'),title('high passed image')
% Note that (1) the boundary smears; (2) there are layer edges
pause

% 5. derivative filtering (Laplacian) using Prewitt operators
% The two masks are:
B=[-1 -1 -1;0 0 0 ;1 1 1]; C=[-1 0 1; -1 0 1; -1 0 1];
% Now filter the image using 2d fitlering command "filter2"
y=abs(filter2(B,x))+abs(filter2(C,x));
figure(2),imagesc(y),colormap('gray'),title('high passed image')
% Note that (1) the boundary smears; (2) there are layer edges
pause

% 6. Medium filter , Gaussian noise
% Let us begin by adding gaussian noise with 0 mean, 0.01 var
%
y=imnoise(x,'gaussian');
figure(1),imshow(y),colormap('gray'),title('add gaussian noise')
% we then use two methods to remove the noise: low pass and medium filter
% First use low pass 3x3 mask

figure(2),imshow(filter2(ones(3,3)/9,y)),colormap('gray'),title('low pass fitlering')
figure(3),imshow(medfilt2(y)),colormap('gray'),title('medium filtered')
pause

% 7. Medium filter, impusive noise
% Let us begin by adding gaussian noise with 0 mean, 0.01 var
%
y=imnoise(x,'salt & pepper');
figure(1),imshow(y),colormap('gray'),title('add gaussian noise')
% we then use two methods to remove the noise: low pass and medium filter
% First use low pass 3x3 mask

figure(2),imshow(filter2(ones(3,3)/9,y)),colormap('gray'),title('low pass fitlering')
figure(3),imshow(medfilt2(y)),colormap('gray'),title('medium filtered')