matlab图像去噪毕业论文

这个够做一篇本科毕业论文了。还是自己做吧。有问题别人可以帮你。但是主要的工作还是得自己做。这个问题没人会帮你回答，我算是牺牲采纳率帮你捧捧场了。

帅哥你好 我也在找这个程序的 全部 您解决了吗 或者用了什么办法 能告诉我吗 急急急急 要交论文了！

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);

中值去噪还是均值去噪？首先选取一个N*N的窗口，比如3*3，对这个窗口内的灰度值进行排序，取中间的那个值，然后在该窗口内所有灰度值统一用这个中值均值就是将窗口内灰度值相加求平均值，窗口内所有的灰度值统一用这个均值要比较结果的话，求峰值信噪比一般来讲，5*5的窗口要比3*3的好。我去年的毕业论文就是写这个。另外你去噪的图像必须是标准的，最好是512*512