SB_ATV.m

function u = SB_ATV(g,mu)
% Split Bregman Anisotropic Total Variation Denoising
%
%   u = arg min_u 1/2||u-g||_2^2 + mu*ATV(u)
%   
%   g : noisy image
%   mu: regularisation parameter
%   u : denoised image
%
% Refs:
%  *Goldstein and Osher, The split Bregman method for L1 regularized problems
%   SIAM Journal on Imaging Sciences 2(2) 2009
%  *Micchelli et al, Proximity algorithms for image models: denoising
%   Inverse Problems 27(4) 2011
%
% Benjamin Trémoulhéac
% University College London
% b.tremoulheac@cs.ucl.ac.uk
% April 2012
g = g(:);
n = length(g);
[B Bt BtB] = DiffOper(sqrt(n));
b = zeros(2*n,1);
d = b;
u = g;
err = 1;k = 1;
tol = 1e-3;
lambda = 1;
while err > tol
    fprintf('it. %g ',k);
    up = u;
  %  size(speye(n))
  %  size(BtB)
    [u,~] = cgs(speye(n)+BtB, g-lambda*Bt*(b-d),1e-5,100); 
    Bub = B*u+b;
    fprintf(' %g ', norm(Bub))
    d = max(abs(Bub)-mu/lambda,1).*sign(Bub);
    b = Bub-d;
    err = norm(up-u)/norm(u);
    fprintf('err=%g \n',err);
    k = k+1;
end
fprintf('Stopped because norm(up-u)/norm(u) <= tol=%.1e\n',tol);
end

function [B Bt BtB] = DiffOper(N)
D = spdiags([-ones(N,1) ones(N,1)], [0 1], N,N+1);
D(:,1) = [];
D(1,1) = 0;
B = [ kron(speye(N),D) ; kron(D,speye(N)) ];
size(B)
Bt = B';
BtB = Bt*B;
end