MATLAB中用wnoise函数测试去噪算法

sqrt_snr=3;

init=231434;

[x,xn]=wnoise(3,11,sqrt_snr,init);

% WNOISE generate

noisy wavelet test data.

% X= WNOISE(FUN,N)

returns values of the test function given by FUN, on a

% 2^N sample of [0,1].

[X,XN] = WNOISE(FUN,N,SQRT_SNR) returns the

% previous vector X

rescaled such that std(x) = SQRT_SNR. The returned

% vector XN contains

the same test vector X corrupted by an additive

Gaussian

% white

noise N(0,1). Then XN has a signal-to-noise ratio of

(SQRT_SNR^2).

% [X,XN] =

WNOISE(FUN,N,SQRT_SNR,INIT) returns previous vectors X and % XN,

but the generator seed is set to INI value.

subplot(3,2,1),plot(x)

title('original

test function')

subplot(3,2,2),plot(xn)

title('noised

function')

%产生一个长为2**11点，包含高斯白噪声的正弦信号，噪声的的标准偏差为3。

lev=5;

xd=wden(x,'heursure','s','one',lev,'sym8');

% [XD,CXD,LXD] =

WDEN(X,TPTR,SORH,SCAL,N,'wname')

% returns a de-noised

version XD of input signal X obtained by thresholding the % wavelet

coefficients. Additional output arguments [CXD,LXD] are the wavelet

% decomposition structure of de-noised signal XD.(WDEN根据信号小波分解%

结构[C,L]对信号进行去噪处理，返回处理信号XD，以及XD的小波分解%

结构

{CXD，LXD})。

% TPTR(contains

threshold selection rule)='heursure',

% 'heursure' is an

heuristic variant of the first option

% (选择基于Stein无偏估计理论的自适应域值的启发式改进)

% SORH ('s' or 'h')

is for soft or hard thresholding(决定域值的使用方式)

% SCAL(='onedefines

multiplicative threshold rescaling:'one' for no

rescaling

%(决定域值是否随噪声变化)

'wname'='sym8'

subplot(3,2,3),plot(xd)

title('One

de-noised function')

%

利用’sym8’小波对信号分解，在分解的第5层上，利用启发式SURE域值选择法对信号去噪。

xd=wden(x,'heursure','s','sln',lev,'sym8');

% 'sln' for rescaling

using a single estimation

% of level noise

based on first level coefficients(根据第一层小波分解的噪声方%

差调整域值)

subplot(3,2,4),plot(xd)

title('Sln

de-noised function')

%

同上’sym8’小波对信号分解条件，但用软SURE域值选择算法对信号去噪。

xd=wden(x,'sqtwolog','s','sln',lev,'sym8');

% for universal

threshold sqrt(2*log(.))(固定域值选择算法去噪).

subplot(3,2,5),plot(xd)

title('Sqtwolog

de-noised function')

%

同上’sym8’小波对信号分解条件，但用固定域值选择算法去噪。

[c,l]=wavedec(x,lev,'sym8');

%

WAVEDEC performs a multilevel 1-D wavelet analysis using either a

specific wavelet 'wname' or a

specific set of wavelet decomposition filters (see WFILTERS).[C,L]

= WAVEDEC(X,N,'wname') returns the wavelet decomposition of the

signal X at level N, using 'wname'. The output decomposition

structure contains the wavelet decomposition vector

C(按照一定顺序存储信号小波分解的近似分量和细节分量的系数)and

the bookkeeping vector L(各近似分量和细节分量系数的长度).