db1-wavelet.rar_db1

load noiswom [swa,swh,swv,swd]=swt2(X,3,'db1'); % 使用db1小波对noiswom图像进行三层静态小波分解 whos % 可以看出，swt2所小波分解同样不改变信号的长度，原来的96×96的图 % 像做了三层分解以后，分解系数是12个96×96的图像。 colormap(map) kp=0; for i=1:3 subplot(3,4,kp+1),image(wcodemat(swa(:,:,i),192)); title(['Approx,cfs,level',num2str(i)]) % 显示第i层近似系数图像，以192字节为单位编码 subplot(3,4,kp+2),image(wcodemat(swh(:,:,i),192)); title(['Horiz.Det.cfs level',num2str(i)]) subplot(3,4,kp+3),image(wcodemat(swv(:,:,i),192)); title(['Vert.Det.cfs level',num2str(i)]) subplot(3,4,kp+4),image(wcodemat(swd(:,:,i),192)); title(['Diag.Det.cfs level',num2str(i)]) kp=kp+4; end %图像分解结束 %图像重构 load noiswom [swa,swh,swv,swd]=swt2(X,3,'db1'); % 使用db1小波对noiswom图像进行三层小波分解 mzero=zeros(size(swd)); A=mzero; A(:,:,3)=iswt2(swa,mzero,mzero,mzero,'db1'); % 使用iswt2的滤波器功能，重建第3层的近似系数，为了避免iswt的合 % 成运算，注意在重建过程中，应保证其他各项系数为零。 H=mzero;V=mzero;D=mzero; for i=1;3 swcfs=mzero;swcfs(:,:,i)=swh(:,:,i); H(:,:,i)=iswt2(mzero,swcfs,mzero,mzero,'db1'); swcfs=mzero;swcfs(:,:,i)=swv(:,:,i); V(:,:,i)=iswt2(mzero,mzero,swcfs,mzero,'db1'); swcfs=mzero;swcfs(:,:,i)=swh(:,:,i); H(:,:,i)=iswt2(mzero,mzero,mzero,swcfs,'db1'); end % 分别重建1~3级的各个细节系数，同样在重建某一吸收的时候，要令其他系数为0 A(:,:,2)=A(:,:,3)+H(:,:,3)+V(:,:,3)+D(:,:,3); A(:,:,1)=A(:,:,2)+H(:,:,2)+V(:,:,2)+D(:,:,2); % 使用递推的方法建立地1层和第2层近似系数 colormap(map) kp=0; for i=1:3 subplot(3,4,kp+1),image(wcodemat(A(:,:,i),192)); title(['第',num2str(i),'层近似系数图像'],'fontsize',6) subplot(3,4,kp+2),image(wcodemat(H(:,:,i),192)); title(['第',num2str(i),'层水平细节系数图像'],'fontsize',6) subplot(3,4,kp+3),image(wcodemat(V(:,:,i),192)); title(['第',num2str(i),'层竖直细节系数图像'],'fontsize',6) subplot(3,4,kp+4),image(wcodemat(D(:,:,i),192)); title(['第',num2str(i),'层对角细节系数图像'],'fontsize',6) kp=kp+4; end