发布时间 : 星期日 文章自适应滤波器设计及Matlab实现，附程序代码更新完毕开始阅读5f82ea0cf78a6529647d5385
if classChanged,
f = changeclass(classin, f);
%使图像矩阵的数据类型与输入时相同 end %%%
%%% Subfunction ParseInputs %%%
function [g, nhood, block, noise, msg] = ParseInputs(varargin) g = []; nhood = [3 3]; block = []; noise = []; msg = '';
switch nargin%表示输入参数个数的系统参数 case 0
msg = 'Too few input arguments.'; return; case 1
% wiener2(I)
g = varargin{1};%把输入的第一个参数赋值给g case 2
g = varargin{1};
switch prod(size(varargin{2}))
case 1
% wiener2(I,noise)
noise = varargin{2}; case 2
% wiener2(I,[m n])
nhood = varargin{2}; otherwise
msg = 'Invalid input syntax'; return; end case 3
g = varargin{1};
if (prod(size(varargin{3})) == 2)
% wiener2(I,[m n],[mblock nblock]) OBSOLETE
warning(['WIENER2(I,[m n],[mblock nblock]) is an obsolete syntax.',...
nhood = varargin{2}; block = varargin{3}; else
% wiener2(I,[m n],noise) nhood = varargin{2}; noise = varargin{3};
'Omit the block size, the image matrix is processed all at once.']);
end case 4
% wiener2(I,[m n],[mblock nblock],noise) OBSOLETE
warning(['WIENER2(I,[m n],[mblock nblock],noise) is an obsolete syntax.',... 'Omit the block size, the image matrix is processed all at once.']); g = varargin{1}; nhood = varargin{2}; block = varargin{3}; noise = varargin{4}; otherwise
msg = 'Too many input arguments.'; return; end
% checking if input image is a truecolor image-not supported by WIENER2 if (ndims(g) == 3)%判断是否为三维图像
msg = 'WIENER2 does not support 3D truecolor images as an input.'; return; end;
if (isempty(block))
block = bestblk(size(g));
附录C
% iterative wiener filter clear all; tic
% orignal image I=checkerboard(2); % num of iterations num_iter=128;
for snr =20% 20:10:40 ? res = size(I,1) % image size res2 = res^2 % image size square %%% degradation %%%
h=[6;1;1;1;1;1;zeros(res2-11,1);1;1;1;1;1]; % blur h=h/sum(h); % normalize Dh= fft(h); % diagonal
for c=1:1 % process RGB components individually f=I; % orignal
f_vec=reshape(f, res2,1); % vector image mn_f=mean(f_vec); % mean
amp_f = norm(f_vec)/res; % amplitude
noise_vec = randn(res2, 1)*amp_f/10^(snr/20); % Guassian noise given SNR Dn = abs(fft(noise_vec )).^2/res2; % psd of noise %%% observation %%% g_vec = noise_vec;
for i=1:res2 % degraded image
g_vec(i) = g_vec(i) + [h(res2-i+2: res2)', h(1:res2-i+1)']*f_vec; % H %is circulant matrix end
mn_g = mean(g_vec);
g_vec = g_vec -mn_g; % forced to be zero-mean Dg = abs(fft(g_vec )).^2/res2; % psd of degraded image
%%%%%%%%%%%% Wiener %%%%%%%%%%%%%%% Df = Dg; % basic iterative