Code covered by the BSD License

# Noise variance estimation

### Damien Garcia (view profile)

23 Oct 2009 (Updated )

EVAR estimates the noise variance from 1-D to N-D data

evar(y)
```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.
%
%   Notes:
%   -----
%   A thin-plate smoothing spline model is used to smooth Y. The model
%   whose generalized cross-validation score is minimal can provide the
%   variance of the additive noise.
%   EVAR is only adapted to evenly-gridded 1-D to N-D data.
%
%   Reference
%   ---------
%   Garcia D, Robust smoothing of gridded data in one and higher dimensions
%   with missing values. Computational Statistics & Data Analysis, 2010.
%   <a
%
%   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));
%   est_var = evar(yn); % estimated variance
%   disp('--- 1-D example ---')
%   disp(['Actual variance: ',num2str(var0)])
%   disp(['Estimated variance: ',num2str(est_var)])
%
%   % 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));
%   est_var = evar(fn); % estimated variance
%   disp('--- 2-D example ---')
%   disp(['Actual variance: ',num2str(var0)])
%   disp(['Estimated variance: ',num2str(est_var)])
%
%   % 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));
%   est_var = evar(fn); % estimated variance
%   disp('--- 3-D example ---')
%   disp(['Actual variance: ',num2str(var0)])
%   disp(['Estimated variance: ',num2str(est_var)])
%
%
%   -- Damien Garcia -- 2008/04, revised 2013/05
%   website: <a
%   href="matlab:web('http://www.biomecardio.com')">www.BiomeCardio.com</a>

error(nargchk(1,1,nargin));
if isempty(y), noisevar = NaN; return, end

y = double(y);

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
y = dctn(y);
y = y(:);

% Squares
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

%% DCTN
function y = dctn(y)

%DCTN N-D discrete cosine transform.
%   Y = DCTN(X) returns the discrete cosine transform of X. The array Y is
%   the same size as X and contains the discrete cosine transform
%   coefficients. This transform can be inverted using IDCTN.
%
%   Reference
%   ---------
%   Narasimha M. et al, On the computation of the discrete cosine
%   transform, IEEE Trans Comm, 26, 6, 1978, pp 934-936.
%
%   -- Damien Garcia -- 2008/06, revised 2011/11
%   -- www.BiomeCardio.com --

sizy = size(y);
y = squeeze(y);
dimy = ndims(y);

% Some modifications are required if Y is a vector
if isvector(y)
dimy = 1;
if size(y,1)==1, y = y.'; end
end

% Weighting vectors
w = cell(1,dimy);
for dim = 1:dimy
n = (dimy==1)*numel(y) + (dimy>1)*sizy(dim);
w{dim} = exp(1i*(0:n-1)'*pi/2/n);
end

% --- DCT algorithm ---
if ~isreal(y)
y = complex(dctn(real(y)),dctn(imag(y)));
else
for dim = 1:dimy
siz = size(y);
n = siz(1);
y = y([1:2:n 2*floor(n/2):-2:2],:);
y = reshape(y,n,[]);
y = y*sqrt(2*n);
y = ifft(y,[],1);
y = bsxfun(@times,y,w{dim});
y = real(y);
y(1,:) = y(1,:)/sqrt(2);
y = reshape(y,siz);
y = shiftdim(y,1);
end
end

y = reshape(y,sizy);

end

```