数字图像处理matlab函数-噪声估计、白平衡、gamma校正等

function noisevar = evar(y) %EVAR Noise variance estimation. % Assuming that the deterministic function Y has additive Gaussian noise, % EVAR(Y) returns an estimated variance of this noise. % % Note: % ---- % A thin-plate smoothing spline model is used to smooth Y. It is assumed % that the model whose generalized cross-validation score is minimal can % provide the variance of the additive noise. A few tests showed that % EVAR works very well with "not too irregular" functions. % % Examples: % -------- % % 1D signal % n = 1e6; x = linspace(0,100,n); % y = cos(x/10)+(x/50); % var0 = 0.02; % noise variance % yn = y + sqrt(var0)*randn(size(y)); % evar(yn) % estimated variance % % % 2D function % [x,y] = meshgrid(0:.01:1); % f = exp(x+y) + sin((x-2*y)*3); % var0 = 0.04; % noise variance % fn = f + sqrt(var0)*randn(size(f)); % evar(fn) % estimated variance % % % 3D function % [x,y,z] = meshgrid(-2:.05:2); % f = x.*exp(-x.^2-y.^2-z.^2); % var0 = 0.6; % noise variance % fn = f + sqrt(var0)*randn(size(f)); % evar(fn) % estimated variance % % % Other examples % Click <a href="matlab:web('http://www.biomecardio.com/matlab/evar.html')">here</a> for more examples % % Note: % ---- % EVAR is only adapted to evenly-gridded 1-D to N-D data. % % See also VAR, STD, SMOOTHN % % -- Damien Garcia -- 2008/04, revised 2010/03 % website: <a % href="matlab:web('http://www.biomecardio.com')">www.BiomeCardio.com</a> error(nargchk(1,1,nargin)); d = ndims(y); siz = size(y); S = zeros(siz); for i = 1:d siz0 = ones(1,d); siz0(i) = siz(i); S = bsxfun(@plus,S,cos(pi*(reshape(1:siz(i),siz0)-1)/siz(i))); end S = 2*(d-S(:)); % N-D Discrete Cosine Transform of Y if exist('dctn','file') y = dctn(y); y = y(:); else error('MATLAB:evar:MissingFunction',... ['DCTN is required. Download <a href="matlab:web(''',... 'http://www.biomecardio.com/matlab/dctn.html'')">DCTN</a>.']) end % S = S.^2; y = y.^2; % Upper and lower bounds for the smoothness parameter N = sum(siz~=1); % tensor rank of the y-array hMin = 1e-6; hMax = 0.99; sMinBnd = (((1+sqrt(1+8*hMax.^(2/N)))/4./hMax.^(2/N)).^2-1)/16; sMaxBnd = (((1+sqrt(1+8*hMin.^(2/N)))/4./hMin.^(2/N)).^2-1)/16; % Minimization of the GCV score fminbnd(@func,log10(sMinBnd),log10(sMaxBnd),optimset('TolX',.1)); function score = func(L) % Generalized cross validation score M = 1-1./(1+10^L*S); noisevar = mean(y.*M.^2); score = noisevar/mean(M)^2; end end