matlab小波變換函數(shù)的總結(jié)與程序

1、小波去噪舉例MATLAB中用wnoise函數(shù)測試去噪算法sqrt_snr=3;init=231434;x,xn=wnoise(3,11,sqrt_snr,init; % 加噪,信噪比為3subplot(3,2,1,plot(xtitle('original test function'subplot(3,2,2,plot(xntitle('noised function'lev=5;xd=wden(x,'heursure','s','one',lev,'sym8'%利用小波對一維信號進行降噪, XD

2、為降噪后的%信號,CXD,LXD為XD的小波分解結(jié)構(gòu)% 's' or 'h'決定閾值的使用方式,SCAL決定閾值是%否隨噪聲變化:'one' 不調(diào)整,'sln'對第一層系%數(shù)的層噪聲分別進行估計和調(diào)整;'mln'對各層%系數(shù)的層噪聲分別進行估計和調(diào)整; subplot(3,2,3,plot(xdtitle('One de-noised function'xd=wden(x,'heursure','s','sln',lev,'sym8's

3、ubplot(3,2,4,plot(xdtitle('Sln de-noised function'xd=wden(x,'sqtwolog','s','sln',lev,'sym8'% 固定閾值選擇算法去噪subplot(3,2,5,plot(xdtitle('Sqtwolog de-noised function'c,l=wavedec(x,lev,'sym8'subplot(3,2,6,plot(xdtitle('CL de-noised function' MA

4、TLAB中圖像噪聲處理舉例load sinsin;colormap('default'subplot(1,3,1,image(X;title('original image'axis('square'init=231434;randn('seed',init;X=X+18*randn(size(X; %產(chǎn)生噪聲信號subplot(1,3,2,image(x;title('noised image'axis('square'thr,sorh,keepapp=ddencmp('den',

5、'wv',x;%自動生成小波去躁或壓縮的閾值選擇方案,也%就是尋找默認值xc,cxc,lxc,perf0,perfl2=wdencmp('gbl',x,'sym4',2,thr,sorh,keepapp;%使用全局閾值進行%圖象降噪subplot(1,3,3,image(xc;title('denoised image'axis('square' 可見,含躁圖像的噪聲含量很強,利用小波去躁,可以有效去除躁聲,同時保留了邊界。Wdencmp函數(shù)xc,cxc,lxc,perf0,perfl2=wdencmp('gbl',x,'sym4',2,thr,sorh,keepapp 是使用小波進行一維或二維小波壓縮或降噪的函數(shù)。前面的語句是對于輸入的一維或二維信號X,使用全局正閾值THR,由小波系數(shù)閾值得到降噪或壓縮后的信號XC。附加的輸出變量cxc,lxc是XC的小波分解結(jié)構(gòu);perf0,pe