多维平滑

shebin

Fast and robust smoothing of one-dimensional and multidimensional data
Syntax   z = smoothn(y)   z = smoothn(y,s)   z = smoothn(y,W)   z = smoothn(y,W,s)   z = smoothn(...,'robust')   z = smoothn(...,'option_name1',option_value1,...)   [z,s,exitflag] = smoothn(...)Description

z = smoothn(y) automatically smoothes the uniformly smpled array y. y can be any N-D noisy array (time series, images, 3D data,...). Non finite data (NaN or Inf) are treated as missing values.

z = smoothn(x,s) smoothes the array y using the smoothness parameter s. Parameter s must be a real positive scalar. The larger s is, the smoother the output will be. If s is omitted (see previous syntax) or empty (i.e. s = []), it is automatically determined using the generalized cross-validation (GCV) method.

z = smoothn(y,W) or z = smoothn(y,W,s) specifies a weighting array W of real positive values, that must have the same size as y. Note that a nil weight corresponds to a missing value.

z = smoothn(...,'robust') carries out a robust smoothing that minimizes the influence of outlying data.

[z,s] = smoothn(...) also returns the calculated value for s so that you can fine-tune the smoothing subsequently if needed.

An iteration process is used in the presence of weighted and/or missing values. z = smoothn(...,Option_Name,Option_Value) smoothes with the termination parameters specified by Option_Name and Option_Value. They can contain three criteria:

• TolZ: Termination tolerance on Z (default = 1e-3), TolZ must be in ]0,1[
• MaxIter: Maximum number of iterations allowed (default = 100)
• Initial: Initial value for the iterative process (default = original data)
  

Syntax: [z,...] = smoothn(...,'MaxIter',50,'TolZ',1e-4,'Initial',z0)

[z,s,exitflag] = smoothn(...) returns a boolean value exitflag that describes the exit condition of smoothn:

• exitflag = 1: smoothn converged.
• exitflag = 0: Maximum number of iterations was reached.
  

Class Support: Input array can be numeric or logical. The returned array is of class double.

Important note

The N-D (inverse) discrete cosine transform functions (dctn.m and idctn.m)are required.

1-D smoothing

Create noisy 1-D data and smooth them using smoothn. The smoothness parameter s is determined automatically. The plot shown below compares regular smoothing with a robust smoothing using the 'robust' option.

x = linspace(0,100,2^8);y = cos(x/10)+(x/50).^2 + randn(size(x))/5;y([70 75 80]) = [5.5 5 6];[z,s] = smoothn(y); % Regular smoothingzr = smoothn(y,'robust'); % Robust smoothingsubplot(121), plot(x,y,'r.',x,z)title('Regular smoothing')subplot(122), plot(x,y,'r.',x,zr)title('Robust smoothing')

Effect of exaggeratedly high smoothness parameter: s is multiplied by 100.

z = smoothn(y,s*100);plot(x,y,'r.',x,z,'k','LineWidth',2)
2-D smoothing

Create a noisy 2-D parametric surface and smooth it using smoothn.

[xp,yp] = deal(0:.02:1);[x,y] = meshgrid(xp,yp);f = exp(x+y) + sin((x-2*y)*3);fn = f + randn(size(f))*0.5; % Adding white noisefs = smoothn(fn); % DCT-based smoothingsubplot(121), surf(xp,yp,fn), zlim([0 8]), axis squaresubplot(122), surf(xp,yp,fs), zlim([0 8]), axis square
2-D smoothing with missing datan = 256;y0 = peaks(n);y = y0 + rand(size(y0))*2;I = randperm(n^2);y(I(1:n^2*0.5)) = NaN; % lose 1/2 of datay(40:90,140:190) = NaN; % create a holez = smoothn(y); % smooth datasubplot(2,2,1:2), imagesc(y), axis equal offtitle('Noisy corrupt data')subplot(223), imagesc(z), axis equal offtitle('Recovered data ...')subplot(224), imagesc(y0), axis equal offtitle('... compared with original data')
3-D smoothing[x,y,z] = meshgrid(-2:.2:2,-2:.2:2,-2:.2:2);xslice = [-0.8,1]; yslice = 2; zslice = [-2,0];vn = x.*exp(-x.^2-y.^2-z.^2) + randn(size(x))*0.06;subplot(121), slice(x,y,z,vn,xslice,yslice,zslice,'cubic')title('Noisy data')v = smoothn(vn);subplot(122), slice(x,y,z,v,xslice,yslice,zslice,'cubic')title('Smoothed data')
Cardioidt = linspace(0,2*pi,1000);x = 2*cos(t).*(1-cos(t)) + randn(size(t))*0.1;y = 2*sin(t).*(1-cos(t)) + randn(size(t))*0.1;z = smoothn(complex(x,y));plot(x,y,'r.',real(z),imag(z),'k','linewidth',2)axis equal tight
Reference

Garcia D, Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 2010;54:1167-1178.

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