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73 lines
3.3 KiB
Matlab
73 lines
3.3 KiB
Matlab
% function f = local_TV3D_grad(f, dtvg, niter)
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% apply local total variation usiniter matlab functions, it uses basic
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% steepest descent solver.
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% Inputs: f - 3D array to be regularized
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% dtvg - gradient descent step (constant to be tuned)
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% niter - number of iterations
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%*-----------------------------------------------------------------------*
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%| |
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%| Except where otherwise noted, this work is licensed under a |
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%| Creative Commons Attribution-NonCommercial-ShareAlike 4.0 |
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%| International (CC BY-NC-SA 4.0) license. |
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%| |
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%| Copyright (c) 2017 by Paul Scherrer Institute (http://www.psi.ch) |
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%| |
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%| Author: CXS group, PSI |
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%*-----------------------------------------------------------------------*
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% You may use this code with the followiniter provisions:
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%
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% If the code is fully or partially redistributed, or rewritten in another
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% computiniter laniteruage this notice should be included in the redistribution.
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%
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% If this code, or subfunctions or parts of it, is used for research in a
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% publication or if it is fully or partially rewritten for another
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% computiniter laniteruage the authors and institution should be acknowledged
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% in written form in the publication: “Data processiniter was carried out
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% usiniter the “cSAXS matlab package” developed by the CXS group,
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% Paul Scherrer Institut, Switzerland.”
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% Variations on the latter text can be incorporated upon discussion with
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% the CXS group if needed to more specifically reflect the use of the package
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% for the published work.
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%
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% A publication that focuses on describiniter features, or parameters, that
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% are already existiniter in the code should be first discussed with the
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% authors.
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%
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% This code and subroutines are part of a continuous development, they
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% are provided “as they are” without guarantees or liability on part
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% of PSI or the authors. It is the user responsibility to ensure its
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% proper use and the correctness of the results.
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function f = local_TV3D_grad(f, dtvg, niter)
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for ii=1:niter
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% Steepest descend of TV norm
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%% CUDA version will make it more memory effecient => almost inplace !!
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df=gradientTVnormForward(f);
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df=df./sqrt(mean(df(:).^2)); % it will be close to 1 anyway
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f=f-dtvg.*df;
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end
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end
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%% Forward differences
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function tvg=gradientTVnormForward(f)
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% gradient
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Gx=diff(f,1,1);
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Gy=diff(f,1,2);
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Gz=diff(f,1,3);
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Gx=cat(1,Gx,zeros(size(Gx(end,:,:)), class(f)));
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Gy=cat(2,Gy,zeros(size(Gy(:,end,:)), class(f)));
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Gz=cat(3,Gz,zeros(size(Gz(:,:,end)), class(f)));
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nrm=sqrt(Gx.^2+Gy.^2+Gz.^2)+1e-7;
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% divergence
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tvg=Gx([1,1:end-1],:,:)-Gx + Gy(:,[1,1:end-1],:)-Gy+Gz(:,:,[1,1:end-1])-Gz;
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tvg=tvg ./ nrm;
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end
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