MATLAB Answers

Fitting Gaussian keeping max amplitude same

조회 수: 2(최근 30일)
Abhishek Saini
Abhishek Saini 2021년 6월 2일
답변: Abhishek Saini 2021년 6월 3일
Hi
I want to fit multi peak data keeping the maximum amplidute same. I tried smoothening and peak fitting but unable to achinve good results. Data looks like the blue line and i want to fit somthing similar to black line. Kindly advise.
Regards
Abhi
  댓글 수: 1
Mathieu NOE
Mathieu NOE 2021년 6월 2일
hello
you have a code (+ data) to share ?
tx

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채택된 답변

Abhishek Saini
Abhishek Saini 2021년 6월 3일
Thanks for your reply.
It help me to finalise the code by keeping the singal max amplitude same and shift the singal at the max amplitude.
close all;clc;
% smooth a curve / narrow peaks removal ---
% Create artificial signal
x=linspace(0,100,256);
y=cos(x/10)+(x/50).^2 + randn(size(x))/10;
figure;
subplot(121),plot(x,y,'r','LineWidth',2)
y([70 75 80]) = [5.5 5 6];
subplot(122),plot(x,y,'r','LineWidth',2)
N = 100; % smoothining points
z = smoothn(y,N); % Regular smoothing
zr = smoothn(y,N,'robust'); % Robust smoothing
figure;
subplot(121), plot(x,y,'r',x,z,'k','LineWidth',2)
axis square, title('Regular smoothing');
subplot(122), plot(x,y,'r',x,zr,'k','LineWidth',2)
axis square, title('Robust smoothing');
% keeping the max same
[yMax, ymaxIdx] = max(y);
[zrMax, zrMaxIdx]= max(zr);
zr=zr.*(yMax/zrMax);
figure;
subplot(121), plot(x,y,'r',x,z,'k','LineWidth',2)
axis square, title('Regular smoothing');
subplot(122), plot(x,y,'r',x,zr,'k','LineWidth',2)
axis square, title('Robust smoothing, max amplitude same');
% Shift signal to right to match center
shift=ymaxIdx-zrMaxIdx;
zrshift = zr(1:end-shift);
zrshift = circshift(zr,shift);
figure; plot(x,y,'r',x,zrshift,'k','LineWidth',2)
axis square, title('Robust smoothing with shifted signal');

추가 답변(2개)

Jeff Miller
Jeff Miller 2021년 6월 3일
One option is to fit a smoothed curve and then multiply the (smoothed, predicted) height by whatever constant is needed to make the smooth curve larger than the jagged data one at all points.

Mathieu NOE
Mathieu NOE 2021년 6월 3일
hello
robust smoothing can help you
see code below
clc
clearvars
%--- Example #1: smooth a curve / narrow peaks removal ---
x = linspace(0,100,256);
y = cos(x/10)+(x/50).^2 + randn(size(x))/10;
y([70 75 80]) = [5.5 5 6];
N = 100;
z = smoothn(y,N); % Regular smoothing
zr = smoothn(y,N,'robust'); % Robust smoothing
subplot(121), plot(x,y,'r',x,z,'k','LineWidth',2)
axis square, title('Regular smoothing')
subplot(122), plot(x,y,'r',x,zr,'k','LineWidth',2)
axis square, title('Robust smoothing')
function [z,s,exitflag] = smoothn(varargin)
%SMOOTHN Robust spline smoothing for 1-D to N-D data.
% SMOOTHN provides a fast, automatized and robust discretized spline
% smoothing for data of arbitrary dimension.
%
% Z = SMOOTHN(Y) automatically smoothes the uniformly-sampled 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(Y,S) smoothes the array Y using the smoothing parameter S.
% S must be a real positive scalar. The larger S is, the smoother the
% output will be. If the smoothing parameter S is omitted (see previous
% option) or empty (i.e. S = []), it is automatically determined by
% minimizing the generalized cross-validation (GCV) score.
%
% Z = SMOOTHN(Y,W) or Z = SMOOTHN(Y,W,S) smoothes Y using a weighting
% array W of positive values, that must have the same size as Y. Note
% that a nil weight corresponds to a missing value.
%
% If you want to smooth a vector field or multicomponent data, Y must be
% a cell array. For example, if you need to smooth a 3-D vectorial flow
% (Vx,Vy,Vz), use Y = {Vx,Vy,Vz}. The output Z is also a cell array which
% contains the smoothed components. See examples 5 to 8 below.
%
% Robust smoothing
% ----------------
% Z = SMOOTHN(...,'robust') carries out a robust smoothing that minimizes
% the influence of outlying data.
%
% [Z,S] = SMOOTHN(...) also returns the calculated value for the
% smoothness parameter 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 the following 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)
% Weights: Weighting function for robust smoothing:
% 'bisquare' (default), 'talworth' or 'cauchy'
% -----------------
% Syntax: [Z,...] = SMOOTHN(...,'MaxIter',500,'TolZ',1e-4,'Initial',Z0);
%
% [Z,S,EXITFLAG] = SMOOTHN(...) returns a boolean value EXITFLAG that
% describes the exit condition of SMOOTHN:
% 1 SMOOTHN converged.
% 0 Maximum number of iterations was reached.
%
% Reference
% ---------
% Garcia D, Robust smoothing of gridded data in one and higher dimensions
% with missing values. Computational Statistics & Data Analysis, 2010.
% <a
% href="matlab:web('http://www.biomecardio.com/pageshtm/publi/csda10.pdf')">PDF download</a>
%
% For velocity vector fields, also refer to:
%
% Garcia D, A fast all-in-one method for automated post-processing of PIV
% data. Exp Fluids, 2011.
% <a
% href="matlab:web('http://www.biomecardio.com/pageshtm/publi/expfluids1010.pdf')">PDF download</a>
%
% Examples:
% --------
% %--- Example #1: smooth a curve ---
% x = linspace(0,100,2^8);
% y = cos(x/10)+(x/50).^2 + randn(size(x))/10;
% y([70 75 80]) = [5.5 5 6];
% z = smoothn(y); % Regular smoothing
% zr = smoothn(y,'robust'); % Robust smoothing
% subplot(121), plot(x,y,'r.',x,z,'k','LineWidth',2)
% axis square, title('Regular smoothing')
% subplot(122), plot(x,y,'r.',x,zr,'k','LineWidth',2)
% axis square, title('Robust smoothing')
%
% %--- Example #2: smooth a surface ---
% xp = 0:.02:1;
% [x,y] = meshgrid(xp);
% f = exp(x+y) + sin((x-2*y)*3);
% fn = f + randn(size(f))*0.5;
% fs = smoothn(fn);
% subplot(121), surf(xp,xp,fn), zlim([0 8]), axis square
% subplot(122), surf(xp,xp,fs), zlim([0 8]), axis square
%
% %--- Example #3: smooth an image with missing data ---
% n = 256;
% y0 = peaks(n);
% y = y0 + randn(size(y0))*2;
% I = randperm(n^2);
% y(I(1:n^2*0.5)) = NaN; % lose 1/2 of data
% y(40:90,140:190) = NaN; % create a hole
% z = smoothn(y); % smooth data
% subplot(2,2,1:2), imagesc(y), axis equal off
% title('Noisy corrupt data')
% subplot(223), imagesc(z), axis equal off
% title('Recovered data ...')
% subplot(224), imagesc(y0), axis equal off
% title('... compared with the actual data')
%
% %--- Example #4: smooth volumetric data ---
% [x,y,z] = meshgrid(-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')
%
% %--- Example #5: smooth a cardioid ---
% t = 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({x,y});
% plot(x,y,'r.',z{1},z{2},'k','linewidth',2)
% axis equal tight
%
% %--- Example #6: smooth a 3-D parametric curve ---
% t = linspace(0,6*pi,1000);
% x = sin(t) + 0.1*randn(size(t));
% y = cos(t) + 0.1*randn(size(t));
% z = t + 0.1*randn(size(t));
% u = smoothn({x,y,z});
% plot3(x,y,z,'r.',u{1},u{2},u{3},'k','linewidth',2)
% axis tight square
%
% %--- Example #7: smooth a 2-D vector field ---
% [x,y] = meshgrid(linspace(0,1,24));
% Vx = cos(2*pi*x+pi/2).*cos(2*pi*y);
% Vy = sin(2*pi*x+pi/2).*sin(2*pi*y);
% Vx = Vx + sqrt(0.05)*randn(24,24); % adding Gaussian noise
% Vy = Vy + sqrt(0.05)*randn(24,24); % adding Gaussian noise
% I = randperm(numel(Vx));
% Vx(I(1:30)) = (rand(30,1)-0.5)*5; % adding outliers
% Vy(I(1:30)) = (rand(30,1)-0.5)*5; % adding outliers
% Vx(I(31:60)) = NaN; % missing values
% Vy(I(31:60)) = NaN; % missing values
% Vs = smoothn({Vx,Vy},'robust'); % automatic smoothing
% subplot(121), quiver(x,y,Vx,Vy,2.5), axis square
% title('Noisy velocity field')
% subplot(122), quiver(x,y,Vs{1},Vs{2}), axis square
% title('Smoothed velocity field')
%
% %--- Example #8: smooth a 3-D vector field ---
% load wind % original 3-D flow
% % -- Create noisy data
% % Gaussian noise
% un = u + randn(size(u))*8;
% vn = v + randn(size(v))*8;
% wn = w + randn(size(w))*8;
% % Add some outliers
% I = randperm(numel(u)) < numel(u)*0.025;
% un(I) = (rand(1,nnz(I))-0.5)*200;
% vn(I) = (rand(1,nnz(I))-0.5)*200;
% wn(I) = (rand(1,nnz(I))-0.5)*200;
% % -- Visualize the noisy flow (see CONEPLOT documentation)
% figure, title('Noisy 3-D vectorial flow')
% xmin = min(x(:)); xmax = max(x(:));
% ymin = min(y(:)); ymax = max(y(:));
% zmin = min(z(:));
% daspect([2,2,1])
% xrange = linspace(xmin,xmax,8);
% yrange = linspace(ymin,ymax,8);
% zrange = 3:4:15;
% [cx cy cz] = meshgrid(xrange,yrange,zrange);
% k = 0.1;
% hcones = coneplot(x,y,z,un*k,vn*k,wn*k,cx,cy,cz,0);
% set(hcones,'FaceColor','red','EdgeColor','none')
% hold on
% wind_speed = sqrt(un.^2 + vn.^2 + wn.^2);
% hsurfaces = slice(x,y,z,wind_speed,[xmin,xmax],ymax,zmin);
% set(hsurfaces,'FaceColor','interp','EdgeColor','none')
% hold off
% axis tight; view(30,40); axis off
% camproj perspective; camzoom(1.5)
% camlight right; lighting phong
% set(hsurfaces,'AmbientStrength',.6)
% set(hcones,'DiffuseStrength',.8)
% % -- Smooth the noisy flow
% Vs = smoothn({un,vn,wn},'robust');
% % -- Display the result
% figure, title('3-D flow smoothed by SMOOTHN')
% daspect([2,2,1])
% hcones = coneplot(x,y,z,Vs{1}*k,Vs{2}*k,Vs{3}*k,cx,cy,cz,0);
% set(hcones,'FaceColor','red','EdgeColor','none')
% hold on
% wind_speed = sqrt(Vs{1}.^2 + Vs{2}.^2 + Vs{3}.^2);
% hsurfaces = slice(x,y,z,wind_speed,[xmin,xmax],ymax,zmin);
% set(hsurfaces,'FaceColor','interp','EdgeColor','none')
% hold off
% axis tight; view(30,40); axis off
% camproj perspective; camzoom(1.5)
% camlight right; lighting phong
% set(hsurfaces,'AmbientStrength',.6)
% set(hcones,'DiffuseStrength',.8)
%
% See also DCTSMOOTH, SMOOTH1Q, DCTN, IDCTN.
%
% -- Damien Garcia -- 2009/03, revised 2013/06
% website: <a
% href="matlab:web('http://www.biomecardio.com')">www.BiomeCardio.com</a>
% Check input arguments
error(nargchk(1,12,nargin));
%% Test & prepare the variables
%---
k = 0;
while k<nargin && ~ischar(varargin{k+1}), k = k+1; end
%---
% y = array to be smoothed
y = varargin{1};
if ~iscell(y), y = {y}; end
sizy = size(y{1});
ny = numel(y); % number of y components
for i = 1:ny
if ~isequal(sizy,size(y{i}))
error('Matlab:smoothn:SizeMismatch',...
'Data arrays must have the same size.')
end
y{i} = double(y{i});
end
noe = prod(sizy); % number of elements
if noe<2, z = y; s = []; exitflag = true; return, end
%---
% Smoothness parameter and weights
W = ones(sizy);
s = [];
if k==2
if isempty(varargin{2}) || isscalar(varargin{2}) % smoothn(y,s)
s = varargin{2}; % smoothness parameter
else % smoothn(y,W)
W = varargin{2}; % weight array
end
elseif k==3 % smoothn(y,W,s)
W = varargin{2}; % weight array
s = varargin{3}; % smoothness parameter
end
if ~isequal(size(W),sizy)
error('MATLAB:smoothn:SizeMismatch',...
'Arrays for data and weights (Y and W) must have same size.')
elseif ~isempty(s) && (~isscalar(s) || s<0)
error('MATLAB:smoothn:IncorrectSmoothingParameter',...
'The smoothing parameter S must be a scalar >=0')
end
%---
% "Maximal number of iterations" criterion
I = find(strcmpi(varargin,'MaxIter'),1);
if isempty(I)
MaxIter = 100; % default value for MaxIter
else
try
MaxIter = varargin{I+1};
catch %#ok
error('MATLAB:smoothn:IncorrectMaxIter',...
'MaxIter must be an integer >=1')
end
if ~isnumeric(MaxIter) || ~isscalar(MaxIter) ||...
MaxIter<1 || MaxIter~=round(MaxIter)
error('MATLAB:smoothn:IncorrectMaxIter',...
'MaxIter must be an integer >=1')
end
end
%---
% "Tolerance on smoothed output" criterion
I = find(strcmpi(varargin,'TolZ'),1);
if isempty(I)
TolZ = 1e-3; % default value for TolZ
else
try
TolZ = varargin{I+1};
catch %#ok
error('MATLAB:smoothn:IncorrectTolZ',...
'TolZ must be in ]0,1[')
end
if ~isnumeric(TolZ) || ~isscalar(TolZ) || TolZ<=0 || TolZ>=1
error('MATLAB:smoothn:IncorrectTolZ',...
'TolZ must be in ]0,1[')
end
end
%---
% "Initial Guess" criterion
I = find(strcmpi(varargin,'Initial'),1);
if isempty(I)
isinitial = false; % default value for TolZ
else
isinitial = true;
try
z0 = varargin{I+1};
catch %#ok
error('MATLAB:smoothn:IncorrectInitialGuess',...
'Z0 must be a valid initial guess for Z')
end
if ~isnumeric(z0) || ~isequal(size(z0),sizy)
error('MATLAB:smoothn:IncorrectTolZ',...
'Z0 must be a valid initial guess for Z')
end
end
%---
% "Weighting function" criterion (for robust smoothing)
I = find(strcmpi(varargin,'Weights'),1);
if isempty(I)
weightstr = 'bisquare'; % default weighting function
else
try
weightstr = lower(varargin{I+1});
catch ME
error('MATLAB:smoothn:IncorrectWeights',...
'A valid weighting function must be chosen')
end
if ~ischar(weightstr)
error('MATLAB:smoothn:IncorrectWeights',...
'A valid weighting function must be chosen')
end
end
%---
% "Order" criterion (by default m = 2)
% Note: m = 0 is of course not recommended!
I = find(strcmpi(varargin,'Order'),1);
if isempty(I)
m = 2; % order
else
try
m = varargin{I+1};
catch %#ok
error('MATLAB:smoothn:IncorrectOrder',...
'A valid order must be chosen')
end
if ~ismember(m,0:2);
error('MATLAB:smoothn:IncorrectOrder',...
'The order must be 0, 1 or 2.')
end
end
%---
% Weights. Zero weights are assigned to not finite values (Inf or NaN),
% (Inf/NaN values = missing data).
IsFinite = isfinite(y{1});
for i = 2:ny, IsFinite = IsFinite & isfinite(y{i}); end
nof = nnz(IsFinite); % number of finite elements
W = W.*IsFinite;
if any(W<0)
error('MATLAB:smoothn:NegativeWeights',...
'Weights must all be >=0')
else
W = W/max(W(:));
end
%---
% Weighted or missing data?
isweighted = any(W(:)<1);
%---
% Robust smoothing?
isrobust = any(strcmpi(varargin,'robust'));
%---
% Automatic smoothing?
isauto = isempty(s);
%% Create the Lambda tensor
%---
% Lambda contains the eingenvalues of the difference matrix used in this
% penalized least squares process (see CSDA paper for details)
d = ndims(y);
Lambda = zeros(sizy);
for i = 1:d
siz0 = ones(1,d);
siz0(i) = sizy(i);
Lambda = bsxfun(@plus,Lambda,...
cos(pi*(reshape(1:sizy(i),siz0)-1)/sizy(i)));
end
Lambda = 2*(d-Lambda);
if ~isauto, Gamma = 1./(1+s*Lambda.^m); end
%% Upper and lower bound for the smoothness parameter
% The average leverage (h) is by definition in [0 1]. Weak smoothing occurs
% if h is close to 1, while over-smoothing appears when h is near 0. Upper
% and lower bounds for h are given to avoid under- or over-smoothing. See
% equation relating h to the smoothness parameter for m = 2 (Equation #12
% in the referenced CSDA paper).
N = sum(sizy~=1); % tensor rank of the y-array
hMin = 1e-6; hMax = 0.99;
if m==0 % Not recommended. For mathematical purpose only.
sMinBnd = 1/hMax^(1/N)-1;
sMaxBnd = 1/hMin^(1/N)-1;
elseif m==1
sMinBnd = (1/hMax^(2/N)-1)/4;
sMaxBnd = (1/hMin^(2/N)-1)/4;
elseif m==2
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;
end
%% Initialize before iterating
%---
Wtot = W;
%--- Initial conditions for z
if isweighted
%--- With weighted/missing data
% An initial guess is provided to ensure faster convergence. For that
% purpose, a nearest neighbor interpolation followed by a coarse
% smoothing are performed.
%---
if isinitial % an initial guess (z0) has been already given
z = z0;
else
z = InitialGuess(y,IsFinite);
end
else
z = cell(size(y));
for i = 1:ny, z{i} = zeros(sizy); end
end
%---
z0 = z;
for i = 1:ny
y{i}(~IsFinite) = 0; % arbitrary values for missing y-data
end
%---
tol = 1;
RobustIterativeProcess = true;
RobustStep = 1;
nit = 0;
DCTy = cell(1,ny);
vec = @(x) x(:);
%--- Error on p. Smoothness parameter s = 10^p
errp = 0.1;
opt = optimset('TolX',errp);
%--- Relaxation factor RF: to speedup convergence
RF = 1 + 0.75*isweighted;
%% Main iterative process
%---
while RobustIterativeProcess
%--- "amount" of weights (see the function GCVscore)
aow = sum(Wtot(:))/noe; % 0 < aow <= 1
%---
while tol>TolZ && nit<MaxIter
nit = nit+1;
for i = 1:ny
DCTy{i} = dctn(Wtot.*(y{i}-z{i})+z{i});
end
if isauto && ~rem(log2(nit),1)
%---
% The generalized cross-validation (GCV) method is used.
% We seek the smoothing parameter S that minimizes the GCV
% score i.e. S = Argmin(GCVscore).
% Because this process is time-consuming, it is performed from
% time to time (when the step number - nit - is a power of 2)
%---
fminbnd(@gcv,log10(sMinBnd),log10(sMaxBnd),opt);
end
for i = 1:ny
z{i} = RF*idctn(Gamma.*DCTy{i}) + (1-RF)*z{i};
end
% if no weighted/missing data => tol=0 (no iteration)
tol = isweighted*norm(vec([z0{:}]-[z{:}]))/norm(vec([z{:}]));
z0 = z; % re-initialization
end
exitflag = nit<MaxIter;
if isrobust %-- Robust Smoothing: iteratively re-weighted process
%--- average leverage
if m==0 % not recommended
h = 1/(1+s);
elseif m==1
h = 1/sqrt(1+4*s);
elseif m==2
h = sqrt(1+16*s);
h = sqrt(1+h)/sqrt(2)/h;
else
error('m must be 0, 1 or 2.')
end
h = h^N;
%--- take robust weights into account
Wtot = W.*RobustWeights(y,z,IsFinite,h,weightstr);
%--- re-initialize for another iterative weighted process
isweighted = true; tol = 1; nit = 0;
%---
RobustStep = RobustStep+1;
RobustIterativeProcess = RobustStep<4; % 3 robust steps are enough.
else
RobustIterativeProcess = false; % stop the whole process
end
end
%% Warning messages
%---
if isauto
if abs(log10(s)-log10(sMinBnd))<errp
warning('MATLAB:smoothn:SLowerBound',...
['S = ' num2str(s,'%.3e') ': the lower bound for S ',...
'has been reached. Put S as an input variable if required.'])
elseif abs(log10(s)-log10(sMaxBnd))<errp
warning('MATLAB:smoothn:SUpperBound',...
['S = ' num2str(s,'%.3e') ': the upper bound for S ',...
'has been reached. Put S as an input variable if required.'])
end
end
if nargout<3 && ~exitflag
warning('MATLAB:smoothn:MaxIter',...
['Maximum number of iterations (' int2str(MaxIter) ') has ',...
'been exceeded. Increase MaxIter option or decrease TolZ value.'])
end
if numel(z)==1, z = z{:}; end
%% GCV score
%---
function GCVscore = gcv(p)
% Search the smoothing parameter s that minimizes the GCV score
%---
s = 10^p;
Gamma = 1./(1+s*Lambda.^m);
%--- RSS = Residual sum-of-squares
RSS = 0;
if aow>0.9 % aow = 1 means that all of the data are equally weighted
% very much faster: does not require any inverse DCT
for kk = 1:ny
RSS = RSS + norm(DCTy{kk}(:).*(Gamma(:)-1))^2;
end
else
% take account of the weights to calculate RSS:
for kk = 1:ny
yhat = idctn(Gamma.*DCTy{kk});
RSS = RSS + norm(sqrt(Wtot(IsFinite)).*...
(y{kk}(IsFinite)-yhat(IsFinite)))^2;
end
end
%---
TrH = sum(Gamma(:));
GCVscore = RSS/nof/(1-TrH/noe)^2;
end
end
function W = RobustWeights(y,z,I,h,wstr)
% One seeks the weights for robust smoothing...
ABS = @(x) sqrt(sum(abs(x).^2,2)); % "abs" in case of complex numbers
r = cellfun(@minus,y,z,'UniformOutput',0); % residuals
r = cellfun(@(x) x(:),r,'UniformOutput',0);
rI = cell2mat(cellfun(@(x) x(I),r,'UniformOutput',0));
MMED = median(rI); % marginal median
AD = ABS(bsxfun(@minus,rI,MMED)); % absolute deviation
MAD = median(AD); % median absolute deviation
%-- Studentized residuals
u = ABS(cell2mat(r))/(1.4826*MAD)/sqrt(1-h);
u = reshape(u,size(I));
if strcmp(wstr,'cauchy')
c = 2.385; W = 1./(1+(u/c).^2); % Cauchy weights
elseif strcmp(wstr,'talworth')
c = 2.795; W = u<c; % Talworth weights
elseif strcmp(wstr,'bisquare')
c = 4.685; W = (1-(u/c).^2).^2.*((u/c)<1); % bisquare weights
else
error('MATLAB:smoothn:IncorrectWeights',...
'A valid weighting function must be chosen')
end
W(isnan(W)) = 0;
% NOTE:
% ----
% The RobustWeights subfunction looks complicated since we work with cell
% arrays. For better clarity, here is how it would look like without the
% use of cells. Much more readable, isn't it?
%
% function W = RobustWeights(y,z,I,h)
% % weights for robust smoothing.
% r = y-z; % residuals
% MAD = median(abs(r(I)-median(r(I)))); % median absolute deviation
% u = abs(r/(1.4826*MAD)/sqrt(1-h)); % studentized residuals
% c = 4.685; W = (1-(u/c).^2).^2.*((u/c)<1); % bisquare weights
% W(isnan(W)) = 0;
% end
end
%% Initial Guess with weighted/missing data
function z = InitialGuess(y,I)
ny = numel(y);
%-- nearest neighbor interpolation (in case of missing values)
if any(~I(:))
z = cell(size(y));
if license('test','image_toolbox')
for i = 1:ny
[z{i},L] = bwdist(I);
z{i} = y{i};
z{i}(~I) = y{i}(L(~I));
end
else
% If BWDIST does not exist, NaN values are all replaced with the
% same scalar. The initial guess is not optimal and a warning
% message thus appears.
z = y;
for i = 1:ny
z{i}(~I) = mean(y{i}(I));
end
warning('MATLAB:smoothn:InitialGuess',...
['BWDIST (Image Processing Toolbox) does not exist. ',...
'The initial guess may not be optimal; additional',...
' iterations can thus be required to ensure complete',...
' convergence. Increase ''MaxIter'' criterion if necessary.'])
end
else
z = y;
end
%-- coarse fast smoothing using one-tenth of the DCT coefficients
siz = size(z{1});
z = cellfun(@(x) dctn(x),z,'UniformOutput',0);
for k = 1:ndims(z{1})
for i = 1:ny
z{i}(ceil(siz(k)/10)+1:end,:) = 0;
z{i} = reshape(z{i},circshift(siz,[0 1-k]));
z{i} = shiftdim(z{i},1);
end
end
z = cellfun(@(x) idctn(x),z,'UniformOutput',0);
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.
%
% Example
% -------
% RGB = imread('autumn.tif');
% I = rgb2gray(RGB);
% J = dctn(I);
% imshow(log(abs(J)),[]), colormap(jet), colorbar
%
% The commands below set values less than magnitude 10 in the DCT matrix
% to zero, then reconstruct the image using the inverse DCT.
%
% J(abs(J)<10) = 0;
% K = idctn(J);
% figure, imshow(I)
% figure, imshow(K,[0 255])
%
% -- Damien Garcia -- 2008/06, revised 2011/11
% -- www.BiomeCardio.com --
y = double(y);
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
%% IDCTN
function y = idctn(y)
%IDCTN N-D inverse discrete cosine transform.
% X = IDCTN(Y) inverts the N-D DCT transform, returning the original
% array if Y was obtained using Y = DCTN(X).
%
% Reference
% ---------
% Narasimha M. et al, On the computation of the discrete cosine
% transform, IEEE Trans Comm, 26, 6, 1978, pp 934-936.
%
% Example
% -------
% RGB = imread('autumn.tif');
% I = rgb2gray(RGB);
% J = dctn(I);
% imshow(log(abs(J)),[]), colormap(jet), colorbar
%
% The commands below set values less than magnitude 10 in the DCT matrix
% to zero, then reconstruct the image using the inverse DCT.
%
% J(abs(J)<10) = 0;
% K = idctn(J);
% figure, imshow(I)
% figure, imshow(K,[0 255])
%
% See also DCTN, IDSTN, IDCT, IDCT2, IDCT3.
%
% -- Damien Garcia -- 2009/04, revised 2011/11
% -- www.BiomeCardio.com --
y = double(y);
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
% Weighing 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
% --- IDCT algorithm ---
if ~isreal(y)
y = complex(idctn(real(y)),idctn(imag(y)));
else
for dim = 1:dimy
siz = size(y);
n = siz(1);
y = reshape(y,n,[]);
y = bsxfun(@times,y,w{dim});
y(1,:) = y(1,:)/sqrt(2);
y = ifft(y,[],1);
y = real(y*sqrt(2*n));
I = (1:n)*0.5+0.5;
I(2:2:end) = n-I(1:2:end-1)+1;
y = y(I,:);
y = reshape(y,siz);
y = shiftdim(y,1);
end
end
y = reshape(y,sizy);
end

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