Image Processing Reference
In-Depth Information
img_mem=(d-mn)./(mx-mn);
f0 = [];
alpha1=0.7;
im_avg=mean2(img11);
% find the mean of the image
alpha2 = im_avg;
m_high=img_mem.^alpha1;
% upper membership level
m_low =img_mem.^(1/alpha1); % lower membership level
% new membership function
mem_hama =
(m_low+m_high+m_low.*m_high*(alpha2-2))./(1+m_low.*m_
high*(alpha2-1));
figure, imshow(mem_hama)
% Type II fuzzy enhanced image
2. A program to produce an intuitionistic fuzzy-enhanced image
image1=imread('knee.jpg');
a1=rgb2gray(image1);
dim=140;
img1=imcrop(a1,[2 1 dim-1 dim-1]);
img=double(img1); mx=max(max(img)); mn=min(min(img));
mem1=(img-mn)./(mx-mn);
mun=[];
% computing Intuitionistic fuzzy membership function
%% finding optimum value of con
for con=1:0.1:10
mem  =1-(1-mem1).^con;
nonmem=(1-mem1).^(con*(con+1));
hes=1-mem-nonmem;
u=0.0; 
for i=1:dim
for j=1:dim  
ent=(2*mem(i,j)*nonmem(i,j)+hes(i,j)^2)/(hes(i,j)^2+
mem(i,j)^2+ nonmem(i,j)^2) +u;
u=ent;
end
end
lin=ent;
lin_ind=[mun;lin];
mun=lin_ind;
lin_ind;
end
lin_ind;
l=max(lin_ind);
[con]=find(l==lin_ind);
fincon= con*0.1+1;
% Intuitionistic fuzzy membership and non-membership functions
newmem =1-(1-mem1).^fincon;
newnonmem =(1-mem1).^(fincon*(fincon+1));
newhes =1-newmem-newnonmem;
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