基于Matlab模拟BEMD图像处理_bemd求bifm-优快云博客

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⛄ 内容介绍

Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture extraction and image filtering, which are widely recognized as a difficult and challenging computer vision problem. We developed an algorithm based on bidimensional empirical mode decomposition (BEMD) to extract features at multiple scales or spatial frequencies. These features, called intrinsic mode functions, are extracted by a sifting process. The bidimensional sifting process is realized using morphological operators to detect regional maxima and thanks to radial basis function for surface interpolation. The performance of the texture extraction algorithms, using BEMD method, is demonstrated in the experiment with both synthetic and natural images.

⛄ 部分代码

% moy_env.m

% BEMD

% J.C. Nunes, Y. Bouaoune, E. Del閏helle, N. Oumar, and Ph. Bunel.

% "Image analysis by bidimensional empirical mode decomposition".

% Image and Vision Computing Journal (IVC), (to appear), 2003.

function [ID,Difference,SD]=moy_env(I,IS,IF,SDmax,SDmax_prec,precision)

%precision=0.08; % pr閏ision du crit鑢e d'arret

I=double(I);

Difference=0; % Si difference entre la moyenne des 2 enveloppes SUP ET INF

% et l'image originale est NULLE ======> Difference=1

[t1 t2]=size(I);

eps=1; % introduit dans le calcul de SD pour 関iter des divisions par z閞o

ID=I; % ID=image r閟idu, on calcule la diff閞ence entre l'image I et la moyenne des 2 enveloppes sup et inf

IS=double(IS); %enveloppe sup

IF=double(IF); %enveloppe inf

IM=(IS+IF)/2; % Moyenne des 2 enveloppes sup et inf

IDif=I-IM; % Difference entre l'image de d閜art et l'image moyenne

%figure,imagesc(IDif),colormap(gray),title('IDif'),truesize

SD=sum((IDif(:).^2)./(I(:).^2+eps));

%SD=sum((IDif(:).^2)./(I(:).^2+eps));

a=abs(SD-SDmax_prec);

b=(precision*SD); % maintenant on prend celui-l?

if a<b

Difference=1;

ID=I;

disp('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% a<b %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')

else

if SD<SDmax

Difference=1;

ID=I;

% figure,imagesc(I),colormap(gray),title('mode obtenu'),truesize

else % Difference=0

Difference=0;

ID=IDif; % Difference entre l'image de d閜art et l'image moyenne

end

clear IMM IS IF Ib IDif

return

⛄ 运行结果

⛄ 参考文献

[1] Nunes J C , Bouaoune Y , ric Deléchelle, et al. Image analysis by bidimensional empirical mode decomposition[J]. Image & Vision Computing, 2003, 21:1019-1026.