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【MATLAB图像融合】[15]一个自适应PCNN的DEMO详解
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出處: Qu Xiao-Bo <qxb_xmu [at] yahoo.com.cn> Aug.28,2008
先上代碼，依然來自屈小波教授：
A
=imread('1.jpg');
B
=imread('2.jpg');
im
=imread('1.jpg');
im1
=imread('2.jpg');
im
=double(im
);
im1
=double(im1
);
[p
,q
]=size(im
);
link_arrange
=5;
np
=20;F_NA
=Normalized(im
);
F_NB
=Normalized(im1
);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
alpha_L
=1;
alpha_Theta
=2.0;
alpha_F
=0.1;
beta
=0; vF
=0.5;
vL
=0.2;
vTheta
=20;
L1
=zeros(p
,q
);
L2
=zeros(p
,q
);
U1
=zeros(p
,q
);
U2
=zeros(p
,q
);
Y1
=zeros(p
,q
);
Y2
=zeros(p
,q
);
F1
=Y1
;
F2
=Y1
;
Y0
=zeros(p
,q
);
Y01
=zeros(p
,q
);
R
=zeros(p
,q
);
Theta1
=zeros(p
,q
);
Theta2
=zeros(p
,q
);% Compute the linking strength
.
center_x
=round(link_arrange
/2);
center_y
=round(link_arrange
/2);
W
=zeros(link_arrange
,link_arrange
);
for i
=1:link_arrange
for j
=1:link_arrange
if (i
==center_x
)&&(j
==center_y
)W(i
,j
)=0;elseW(i
,j
)=1./sqrt((i
-center_x
).^2+(j
-center_y
).^2);endend
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
I1
=F_NA
;
I2
=F_NB
;
for n
=1:np
[a
,b
]=size(W
);for i
=1:p
for j
=1:q
for c
=1:a
for d
=1:bbeta
=beta
+[im(c
,d
)-im(c
+1,d
)].^2+[im(c
,d
)-im(c
,d
+1)].^2;endend
K1(i
,j
)=conv2(Y1(i
,j
),W
,'same');K2(i
,j
)=conv2(Y2(i
,j
),W
,'same');F1(i
,j
)=exp(-alpha_F
)*F1(i
,j
)+vF
*K1(i
,j
)+I1(i
,j
);F2(i
,j
)=exp(-alpha_F
)*F2(i
,j
)+vF
*K2(i
,j
)+I2(i
,j
);L1(i
,j
)=exp(-alpha_L
)*L1(i
,j
)+vL
*K1(i
,j
);L2(i
,j
)=exp(-alpha_L
)*L2(i
,j
)+vL
*K2(i
,j
);Theta1(i
,j
)=exp(-alpha_Theta
)*Theta1(i
,j
)+vTheta
*Y1(i
,j
);Theta2(i
,j
)=exp(-alpha_Theta
)*Theta2(i
,j
)+vTheta
*Y2(i
,j
);U1(i
,j
)=F1(i
,j
).*(1+beta
*L1(i
,j
));U2(i
,j
)=F2(i
,j
).*(1+beta
*L2(i
,j
));Y1(i
,j
)=im2double(U1(i
,j
)>Theta1(i
,j
));Y2(i
,j
)=im2double(U2(i
,j
)>Theta2(i
,j
));Y0(i
,j
)=Y0(i
,j
)+Y1(i
,j
);Y01(i
,j
)=Y01(i
,j
)+Y2(i
,j
);endendY0
=medfilt2(Y0
,[3,3]);Y01
=medfilt2(Y01
,[3,3]);for i
=1:p
for j
=1:q
if abs(Y0(i
,j
)-Y01(i
,j
))<=0.015R(i
,j
)=(I1(i
,j
)+I2(i
,j
))/2;end
if abs(Y0(i
,j
)-Y01(i
,j
))>0.015 &&Y0(i
,j
)>Y01(i
,j
)R(i
,j
)=I1(i
,j
);end
if abs(Y0(i
,j
)-Y01(i
,j
))>0.015 && Y0(i
,j
)<Y01(i
,j
)R(i
,j
)=I2(i
,j
);end endend
end
subplot
131;imshow(A
);
subplot
132;imshow(B
);
subplot
133;imshow(R
);
function [normalized_matrix
,cmin
,cmax
]=Normalized(matrix
)
input_matrix
=abs(matrix
);
Max_input
=max(input_matrix(:));
Min_input
=min(input_matrix(:));
min_matrix
=ones(size(input_matrix
)).*Min_input
;
normalized_matrix
=(input_matrix
-min_matrix
)./(Max_input
-Min_input
+eps
);
cmin
=Min_input
;
cmax
=Max_input
;
end
;
對本代碼進行以下解釋：
①、本代碼基于PCNN，而不是S-PCNN。
②、PCNN的基本原理在前章有解釋。
③、算法的基本流程是歸一化；定義PCNN的如下參數：W,LINK RANGE,TIME,aF,vF,aL,vL,aT,vT;定義EOL（energy of laplacian）作為β的自適應值；通過閾值對點火圖進行融合得到融合圖像。
④、歸一化函數算法：選出一個最大值和最小值；讓矩陣中所有的數值都減去最小值；得到的新矩陣再除以（最大值-最小值）就得到歸一化結果；過程中取絕對值計算。
⑤、權值（W）和鏈接域（LINK RANGE）：通常取奇數，經測試，在W=3時算法卻莫名其妙無法計算結果，檢查無果，W=5,6,7等時都可正常運行。
⑥、需要注意，步驟③最終用點火圖完成融合，不是把點火圖融合在一起，而是根據點火圖的情況去相應地對源圖像像素進行融合操作，故PCNN圖像融合也屬于像素級的圖像融合。
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