Question

图像的亚像素边缘检测 MATLAB代码

求助亚像素边缘检测的程序
本人急需亚像素边缘检测算法的程序，只要是亚像素边缘检测的都可以。帮帮忙！ 我这有基于小波的， 谁能给改成基于小波的图像亚像素边缘检测，发我邮箱：410756340@qq.com
%装载并显示原始图像
load bust;
%加入含噪
init=2055615866;
randn('seed',init);
X1=X+20*randn(size(X));
subplot(221);image(X1);
%colormap(map);
title('原始图像');axis square;
%用小波db4对图像X进行一层小波包分解
T=wpdec2(X1,1,'db4');
%重构图像近似部分
A=wprcoef(T,[1 0]);
subplot(222);image(A);
title('图像的近似部分');
axis square;
%边缘检测
%%原图像的边缘检测
BW1 = edge(A,'sobel');
subplot(223);imshow(BW1);
title('原图像的边缘');
axis square;
%图像近似部分的边缘检测
BW2= edge(X1,'sobel');
subplot(224);imshow(BW2);
title('图像近似部分的边缘');
axis square;
做出来追加100分
做出来追加100分
哥您是会做 专家级的？？我是看不懂 对不对 您给个准信 行吗？？？？？？？谢谢了

Answer 1

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');
% Denoise noisy signal using fixed form threshold with
% a single level estimation of noise standard deviation.

xd = wden(x,'sqtwolog','s','sln',lev,'sym8');
% Denoise noisy signal using fixed minimax threshold with
% a multiple level estimation of noise standard deviation.

xd = wden(x,'minimaxi','s','sln',lev,'sym8');
% If many trials are necessary, it is better to perform
% decomposition one time and threshold it many times :

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

% threshold the decomposition structure [c,l].
xd = wden(c,l,'minimaxi','s','sln',lev,'sym8');
% Load electrical signal and select a part.

load leleccum; indx = 2600:3100;
x = leleccum(indx);
% Use wdencmp for signal de-noising.

% find default values (see ddencmp).
[thr,sorh,keepapp] = ddencmp('den','wv',x);

% denoise signal using global thresholding option.
xd = wdencmp('gbl',x,'db3',2,thr,sorh,keepapp);
% Some trial examples without commands counterpart.

% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 5;
% [xref,x] = wnoise(1,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).

% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 4;
% [xref,x] = wnoise(2,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).

% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).

% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).

% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);
% Some trial examples without commands counterpart (more).

% Rand initialization: init = 2055615866;
% Square root of signal to noise ratio: sqrt_snr = 3;
% [xref,x] = wnoise(3,11,sqrt_snr,init);