用matlab实现基于边缘检测的图象小波阈值去噪方法

来源：百度知道 编辑：UC知道 时间：2024/06/08 06:08:15
(1) 对加噪后的Lena 图进行小波变换;
(2) 计算各个高分辨率子带图象的去噪阈值: ；
(3) 对非边缘特征点的小波系数进行阈值去噪W=W ｜W｜>T或W=0 ｜W｜<T
(4) 对边缘特征点所对应的小波系数以及绝对值大于阈值的小波系数进行消除加性噪声处理, 也即每个系数都减去一个很小的值, 这个值代表叠加在这些系数上的加性噪声;
(5) 进行小波反变换, 得到去噪后的图象.

Press the "Start" button to see a demonstration of
denoising tools in the Wavelet Toolbox.

This demo uses Wavelet Toolbox functions.
% Set signal to noise ratio and set rand seed.
sqrt_snr = 3; init = 2055615866;
% Generate original signal and a noisy version adding
% a standard Gaussian white noise.
[xref,x] = wnoise(3,11,sqrt_snr,init);
% Denoise noisy signal using soft heuristic SURE thresholding
% and scaled noise option, on detail coefficients obtained
% from the decomposition of x, at level 5 by sym8 wavelet.
% Generate original signal and a noisy version adding
% a standard Gaussian white noise.
lev = 5;
xd = wden(x,'heursure','s','one',lev,'sym8');
% Denoise noisy signal using soft SURE thresholding.

xd = wden(x,'rigrsure','s','one',lev,'sym8');
% De