求小波变换图像融合的MATLAB程序，毕设用。

clc

clear all

close all % 清理工作空间

clear

[imA,map1] = imread('A.tif')

M1 = double(imA) / 256

[imB,map2] = imread('B.tif')

M2 = double(imB) / 256

zt= 4

wtype = 'haar'

%M1 - input image A

%M2 - input image B

%wtype使用的小波类型

%Y - fused image

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%

%% 小波变换图像融合

%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% 小波变换的绝对值大的小波系数，对应着显著的亮度变化，也就是图像中的显著特征。所以，选择绝对值大

%% 的小波系数作为我们需要的小波系数。【注意，前面取的是绝对值大小，而不是实际数值大小】

%%

%% 低频部分系数采用二者求平均的方法

%%

%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[c0,s0] = wavedec2(M1, zt, wtype)%多尺度二维小波分解

[c1,s1] = wavedec2(M2, zt, wtype)%多尺度二维小波分解

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% 后面就可以进行取大进行处理。然后进行重构，得到一个图像

%% 的小波系数，然后重构出总的图像效果袭羡。

%% 取绝对值大的小波系数，作为融合后的小波系数

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

KK = size(c1)

Coef_Fusion = zeros(1,KK(2))

Temp = zeros(1,2)

Coef_Fusion(1:s1(1,1)) = (c0(1:s1(1,1))+c1(1:s1(1,1)))/2 %低频系数的处理

%这儿，连高频系数一起处理了，但是后面处理高频系闭禅银数的时候，会将结果覆盖，所以没有关系

%处理高频系数

MM1 = c0(s1(1,1)+1:KK(2))

MM2 = c1(s1(1,1)+1:KK(2))

mm = (abs(MM1)) >(abs(MM2))

Y = (mm.*MM1) + ((~mm).*MM2)

Coef_Fusion(s1(1,1)+1:KK(2)) = Y

%处理高频系数end

%重构

Y = waverec2(Coef_Fusion,s0,wtype)

%显示图像

subplot(2,2,1)imshow(M1)

colormap(gray)

title('input2')

axis square

subplot(2,2,2)imshow(M2)

colormap(gray)

title('input2')

axis square

subplot(223)imshow(Y,[])

colormap(gray)

title('融轿宴合图像')

axis square

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function lap_fusion()

%Laplacian Pyramid fusion

mul= imread('images\ms1.png')

pan= imread('images\pan.png')

figure(1)

imshow(mul)title('MS原始宴裂图像')axis fill

figure(2)

imshow(pan)title('Pan原始图像')axis fill

mul = double(rgb2gray(mul))/255

pan = double(rgb2gray(pan))/255

%普拉斯金塔变换参数

mp = 1zt =4cf =1ar = 1cc = [cf ar]

Y_lap = fuse_lap(mul,pan,zt,cc,mp)

figure(3)

imshow(Y_lap)title('lap fusion 后的图像')axis fill

imwrite(Y_lap,'images\lap fusion后的图像.jpg','Quality',100)

%main function end

function Y = fuse_lap(M1, M2, zt, ap, mp)

%Y = fuse_lap(M1, M2, zt, ap, mp) image fusion with laplacian pyramid

%

%M1 - input image A

%M2 - input image B

%zt - maximum decomposition level

%ap - coefficient selection highpass (see selc.m)

%mp - coefficient selection base image (see selb.m)

%

%Y - fused image

%(Oliver Rockinger 16.08.99)

% check inputs

[z1 s1] = size(M1)

[z2 s2] = size(M2)

if (z1 ~= z2) | (s1 ~= s2)

error('Input images are not of same size')

end

% define filter

w = [1 4 6 4 1] / 16

% cells for selected images

E = cell(1,zt)

% loop over decomposition depth ->analysis

for i1 = 1:zt

% calculate and store actual image size

[z s] = size(M1)

zl(i1) = zsl(i1) = s

% check if image expansion necessary

if (floor(z/2) ~= z/2), ew(1) = 1else, ew(1) = 0end

if (floor(s/2) ~= s/2), ew(2) = 1else, ew(2) = 0end

% perform expansion if necessary

if (any(ew))

M1 = adb(M1,ew)

M2 = adb(M2,ew)

end

% perform filtering

G1 = conv2(conv2(es2(M1,2), w, 'valid'),w', 'valid')

G2 = conv2(conv2(es2(M2,2), w, 'valid'),w', 'valid'友祥哗)

% decimate, undecimate and interpolate

M1T = conv2(conv2(es2(undec2(dec2(G1)), 2), 2*w, 'valid'),2*w', 'valid')

M2T = conv2(conv2(es2(undec2(dec2(G2)), 2), 2*w, 'valid'),2*w', '好行valid')

% select coefficients and store them

E(i1) = {selc(M1-M1T, M2-M2T, ap)}

% decimate

M1 = dec2(G1)

M2 = dec2(G2)

end

% select base coefficients of last decompostion stage

M1 = selb(M1,M2,mp)

% loop over decomposition depth ->synthesis

for i1 = zt:-1:1

% undecimate and interpolate

M1T = conv2(conv2(es2(undec2(M1), 2), 2*w, 'valid'), 2*w', 'valid')

% add coefficients

M1 = M1T + E{i1}

% select valid image region

M1 = M1(1:zl(i1),1:sl(i1))

end

% copy image

Y = M1

function Y = es2(X, n)

%Y = ES2(X, n) symmetric extension of a matrix on all borders

%

%X - input matrix

%n - number of rows/columns to extend

%

%Y - extended matrix

%(Oliver Rockinger 16.08.99)

[z s] = size(X)

Y= zeros(z+2*n, s+2*n)

Y(n+1:n+z,n:-1:1)= X(:,2:1:n+1)

Y(n+1:n+z,n+1:1:n+s) = X

Y(n+1:n+z,n+s+1:1:s+2*n) = X(:,s-1:-1:s-n)

Y(n:-1:1,n+1:s+n)= X(2:1:n+1,:)

Y(n+z+1:1:z+2*n,n+1:s+n) = X(z-1:-1:z-n,:)

function Y = dec2(X)

%Y = dec2(X) downsampling of a matrix by 2

%

%X - input matrix

%

%Y - output matrix

%(Oliver Rockinger 16.08.99)

[a b] = size(X)

Y = X(1:2:a, 1:2:b)

function Y = undec2(X)

%Y = undec2(X) upsampling of a matrix by 2

%

%X - input matrix

%

%Y - output matrix

%(Oliver Rockinger 16.08.99)

[z s] = size(X)

Y = zeros(2*z, 2*s)

Y(1:2:2*z,1:2:2*s) = X

function Y = selb(M1, M2, mp)

%Y = selb(M1, M2, mp) coefficient selection for base image

%

%M1 - coefficients A

%M2 - coefficients B

%mp - switch for selection type

% mp == 1: select A

% mp == 2: select B

% mp == 3: average A and B

%

%Y - combined coefficients

%(Oliver Rockinger 16.08.99)

switch (mp)

case 1, Y = M1

case 2, Y = M2

case 3, Y = (M1 + M2) / 2

otherwise, error('unknown option')

end

function Y = selc(M1, M2, ap)

%Y = selc(M1, M2, ap) coefficinet selection for highpass components

%

%M1 - coefficients A

%M2 - coefficients B

%mp - switch for selection type

% mp == 1: choose max(abs)

% mp == 2: salience / match measure with threshold == .75 (as proposed by Burt et al)

% mp == 3: choose max with consistency check (as proposed by Li et al)

% mp == 4: simple choose max

%

%Y - combined coefficients

%(Oliver Rockinger 16.08.99)

% check inputs

[z1 s1] = size(M1)

[z2 s2] = size(M2)

if (z1 ~= z2) | (s1 ~= s2)

error('Input images are not of same size')

end

% switch to method

switch(ap(1))

case 1,

% choose max(abs)

mm = (abs(M1)) >(abs(M2))

Y = (mm.*M1) + ((~mm).*M2)

case 2,

% Burts method

um = ap(2)th = .75

% compute salience

S1 = conv2(es2(M1.*M1, floor(um/2)), ones(um), 'valid')

S2 = conv2(es2(M2.*M2, floor(um/2)), ones(um), 'valid')

% compute match

MA = conv2(es2(M1.*M2, floor(um/2)), ones(um), 'valid')

MA = 2 * MA ./ (S1 + S2 + eps)

% selection

m1 = MA >thm2 = S1 >S2

w1 = (0.5 - 0.5*(1-MA) / (1-th))

Y = (~m1) .* ((m2.*M1) + ((~m2).*M2))

Y = Y + (m1 .* ((m2.*M1.*(1-w1))+((m2).*M2.*w1) + ((~m2).*M2.*(1-w1))+((~m2).*M1.*w1)))

case 3,

% Lis method

um = ap(2)

% first step

A1 = ordfilt2(abs(es2(M1, floor(um/2))), um*um, ones(um))

A2 = ordfilt2(abs(es2(M2, floor(um/2))), um*um, ones(um))

% second step

mm = (conv2((A1 >A2), ones(um), 'valid')) >floor(um*um/2)

Y = (mm.*M1) + ((~mm).*M2)

case 4,

% simple choose max

mm = M1 >M2

Y = (mm.*M1) + ((~mm).*M2)

otherwise,

error('unkown option')

end

我给你一个凳猜程序：

load wbarb

X1=X

map1=map

subplot(2,2,1)

image(X1)colormap(map1)

title('original image wbarb')

load woman

X2=Xmap2=map

subplot(2,2,2)

image(X2)colormap(map2)

title('original image woman')

[c1,l1]=wavedec2(X1,2,'db3')

[c2,l2]=wavedec2(X2,2,'db3')

c=c1+c2 %fusing the two decomposition coefficients

XX=waverec2(c,l1,'db3'猛乱)

subplot(2,2,3)

image(XX)

title('fusing image I')

Csize1=size(c1)

for i=1:Csize1(2)

c1(i)=1.2*c1(i)

end

Csize2=size(c2)

for j=1:Csize1(2)

c2(i)=0.8*c2(j)

end

c=0.5*(c1+c2)

XXX=waverec2(c,l2,'db3')

subplot(2,2,4)

image(XXX)

title('枣知型fusing image II')

这个可以用，直接运行就好

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