rpca.m

%MATLAB code，子函数
function [A_hat E_hat ] =rpca(res)
%res:输入图像;输出为低秩A_hat和稀疏E_hat
% Copyright (C) 2016,4, all rights reserved.
[row col] = size(res);
lambda = 1/ sqrt(max(size(res)));%
tol = 1e-7;
maxIter = 1000;
% initialize
Y = res;
[u,s,v]=svd(Y);
norm_two=s(1);
norm_inf=max(abs(Y(:)))/lambda;
dual_norm = max(norm_two, norm_inf);
Y = Y / dual_norm;


A_hat = zeros( row, col);
E_hat = zeros( row, col);
mu = 0.01/norm_two; % this one can be tuned
mu_bar = mu * 1e7;
rho = 1.9 ;  % this one can be tuned
d_norm=sqrt(sum(res(:).^2));
iter = 0;
total_svd = 0;
converged = 0;%收敛
stopCriterion = 1;
sv = 10;
    while ~converged
    iter = iter + 1;
    temp_T = res - A_hat + (1/mu)*Y;
    E_hat=temp_T - lambda/mu;
    n1=find(E_hat<0);
    E_hat(n1)=0;
    tmp=temp_T + lambda/mu;
    n1=find(tmp>0);
    tmp(n1)=0;
    E_hat= E_hat+tmp;
    [U1 S1 V1] = svd(res - E_hat + (1/mu)*Y);
%     if chsvd(col, sv) == 1
        U=U1(:,1:sv);
        S=S1(:,1:sv);
        V=V1(:,1:sv);
%     end
    diagS = diag(S);
    svp = length(find(diagS > 1/mu));
    if svp < sv
     sv = min(svp + 1, col);
    else
     sv = min(svp + round(0.05*col), col);
    end


% ? ? A_hat = U(:, 1:svp) * diag(diagS(1:svp) - 1/mu) * V(:, 1:svp)';
U2=U(:, 1:svp);
S2=diag(diagS(1:svp) - 1/mu);
V2=V(:, 1:svp)';
 A_hat=U2*S2*V2;
 total_svd = total_svd + 1;

 Z = res - A_hat - E_hat;

 Y = Y + mu*Z;
 mu = min(mu*rho, mu_bar);

% stop Criterion
stopCriterion = sqrt(sum(Z(:).^2)) / d_norm;
if stopCriterion < tol
converged = 1;
 end
if ~converged && iter >= maxIter
 disp('Maximum iterations reached') ;
 converged = 1 ;
 end
end
end