基于Matlab模拟BEMD图像处理

原创于 2023-04-05 00:42:15 发布 · 262 阅读

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文章介绍了将二维经验模式分解（BEMD）应用于纹理提取和图像过滤的新算法。通过使用形态学算子检测区域最大值和径向基函数进行表面插值的二维筛选过程，提取出称为内在模式函数的特征。实验表明，BEMD方法在处理合成和自然图像的纹理提取上表现出色。

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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

基于Matlab模拟BEMD图像处理_sed

基于Matlab模拟BEMD图像处理_sed_02

基于Matlab模拟BEMD图像处理_Image_03

⛄ 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.