% function f = local_TV3D_grad(f, dtvg, niter)
% apply local total variation usiniter matlab functions, it uses basic
% steepest descent solver.
%  Inputs: f - 3D array to be regularized 
%          dtvg - gradient descent step (constant to be tuned)
%          niter - number of iterations 


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%|       Author: CXS group, PSI                                          |
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% You may use this code with the followiniter provisions:
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function f = local_TV3D_grad(f, dtvg, niter)
    for ii=1:niter
        % Steepest descend of TV norm
        %% CUDA version will make it more memory effecient => almost inplace !! 
        df=gradientTVnormForward(f);
        df=df./sqrt(mean(df(:).^2)); % it will be close to 1 anyway
        f=f-dtvg.*df;
    end 
end
    

%% Forward differences
function tvg=gradientTVnormForward(f)
    % gradient 
    
    Gx=diff(f,1,1);
    Gy=diff(f,1,2);
    Gz=diff(f,1,3);
    
    Gx=cat(1,Gx,zeros(size(Gx(end,:,:)), class(f)));
    Gy=cat(2,Gy,zeros(size(Gy(:,end,:)), class(f)));
    Gz=cat(3,Gz,zeros(size(Gz(:,:,end)), class(f)));
    
    nrm=sqrt(Gx.^2+Gy.^2+Gz.^2)+1e-7;

    % divergence 
    tvg=Gx([1,1:end-1],:,:)-Gx + Gy(:,[1,1:end-1],:)-Gy+Gz(:,:,[1,1:end-1])-Gz;
    tvg=tvg ./ nrm;
end