matlab code for curvelet based image fusion
#1

Hi am yogesh i would like to get details on matlab code for curvelet based image fusion ..My friend prasad said matlab code for curvelet based image fusion will be available here and now i am living at mumbai now am doing master's need help
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#2
Fusion of two images
Syntax
XFUS = wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH)
[XFUS,TXFUS,TX1,TX2] = wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH)
wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH,FLAGPLOT)

Description
The principle of image fusion using wavelets is to merge the wavelet decompositions of the two original images using fusion methods applied to approximations coefficients and details coefficients (see Zeeuw and Misiti et al.).

XFUS = wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH) returns the fused image XFUS obtained by fusion of the two original images X1 and X2. Each fusion method, defined by AFUSMETH and DFUSMETH, merges in a specific way detailed below, the decompositions of X1 and X2, at level LEVEL and using wavelet WNAME.

AFUSMETH and DFUSMETH define the fusion method for approximations and details, respectively.

[XFUS,TXFUS,TX1,TX2] = wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH) returns, in addition to matrix XFUS, three objects of the class WDECTREE associated with XFUS, X1, and X2 respectively (see @WDECTREE). wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH,FLAGPLOT) also plots the objects TXFUS, TX1, and TX2.

Fusmeth denotes AFUSMETH or DFUSMETH. Available fusion methods are

Simple — Fusmeth can be 'max', 'min', 'mean', 'img1', 'img2' or 'rand', which merges the two approximations or details structures obtained from X1 and X2 elementwise by taking the maximum, the minimum, the mean, the first element, the second element, or a randomly chosen element
Parameter-dependent — Fusmeth is of the following form
Fusmeth = struct('name',nameMETH,'param',paramMETH)
where nameMETH can be

'linear'
'UD_fusion'
Up-down fusion
'DU_fusion'
Down-up fusion
'RL_fusion'
Right-left fusion
'UserDEF'
User-defined fusion
For the description of these options and the paramMETH parameter, see wfusmat.

Examples
The following three examples examine the process of image fusion

The first example merges two different images leading to a new image
The second example restores an image from two fuzzy versions of an original image.
The third example shows how to make an image fusion using a user defined fusion method.
% Example 1: Fusion of two different images

% Load two original images: a mask and a bust
load mask; X1 = X;
load bust; X2 = X;

% Merge the two images from wavelet decompositions at level 5
% using db2 by taking two different fusion methods

% fusion by taking the mean for both approximations and details
XFUSmean = wfusimg(X1,X2,'db2',5,'mean','mean');

% fusion by taking the maximum for approximations and the
% minimum for the details
XFUSmaxmin = wfusimg(X1,X2,'db2',5,'max','min');

% Plot original and synthesized images
colormap(map);
subplot(221), image(X1), axis square, title('Mask')
subplot(222), image(X2), axis square, title('Bust')
subplot(223), image(XFUSmean), axis square,
title('Synthesized image, mean-mean')
subplot(224), image(XFUSmaxmin), axis square,
title('Synthesized image, max-min')
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#3

Description
The principle of image fusion using wavelets is to merge the wavelet decompositions of the two original images using fusion methods applied to approximations coefficients and details coefficients (see Zeeuw and Misiti et al.).

XFUS = wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH) returns the fused image XFUS obtained by fusion of the two original images X1 and X2. Each fusion method, defined by AFUSMETH and DFUSMETH, merges in a specific way detailed below, the decompositions of X1 and X2, at level LEVEL and using wavelet WNAME.

AFUSMETH and DFUSMETH define the fusion method for approximations and details, respectively.

[XFUS,TXFUS,TX1,TX2] = wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH) returns, in addition to matrix XFUS, three objects of the class WDECTREE associated with XFUS, X1, and X2 respectively (see @WDECTREE). wfusimg(X1,X2,WNAME,LEVEL,AFUSMETH,DFUSMETH,FLAGPLOT) also plots the objects TXFUS, TX1, and TX2.

Fusmeth denotes AFUSMETH or DFUSMETH. Available fusion methods are

Simple — Fusmeth can be 'max', 'min', 'mean', 'img1', 'img2' or 'rand', which merges the two approximations or details structures obtained from X1 and X2 elementwise by taking the maximum, the minimum, the mean, the first element, the second element, or a randomly chosen element
Parameter-dependent — Fusmeth is of the following form
Fusmeth = struct('name',nameMETH,'param',paramMETH)
where nameMETH can be

'linear'
'UD_fusion'
Up-down fusion
'DU_fusion'
Down-up fusion
'RL_fusion'
Right-left fusion
'UserDEF'
User-defined fusion
For the description of these options and the paramMETH parameter, see wfusmat.

Examples
The following three examples examine the process of image fusion

The first example merges two different images leading to a new image
The second example restores an image from two fuzzy versions of an original image.
The third example shows how to make an image fusion using a user defined fusion method.
% Example 1: Fusion of two different images

% Load two original images: a mask and a bust
load mask; X1 = X;
load bust; X2 = X;

% Merge the two images from wavelet decompositions at level 5
% using db2 by taking two different fusion methods

% fusion by taking the mean for both approximations and details
XFUSmean = wfusimg(X1,X2,'db2',5,'mean','mean');

% fusion by taking the maximum for approximations and the
% minimum for the details
XFUSmaxmin = wfusimg(X1,X2,'db2',5,'max','min');

% Plot original and synthesized images
colormap(map);
subplot(221), image(X1), axis square, title('Mask')
subplot(222), image(X2), axis square, title('Bust')
subplot(223), image(XFUSmean), axis square,
title('Synthesized image, mean-mean')
subplot(224), image(XFUSmaxmin), axis square,
title('Synthesized image, max-min')
Reply

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