function Aest = solver(imageSize, queryLimit)
nmeas = max(1,floor(imageSize/15))
for i=1:nmeas
v = rand(imageSize);
vt1 = v < 0.5;
mask = vt1;
vt2=double(vt1);
pixelSum = queryImage(mask);
B(i,:) = reshape(vt2,imageSize*imageSize,1);
y(i) = pixelSum;
end
sigma_min = max(1,floor(imageSize/2));
[xx,dd] = cosamp(B, y',sigma_min,1.0e-4);
Aest = reshape(xx,imageSize,imageSize);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [Sest,d]=cosamp(Phi,u,K,tol1)
% Cosamp algorithm
% Input
% K : sparsity of Sest
% Phi : measurement matrix
% u: measured vector
% tol1 : tolerance for approximation between successive solutions.
% Output
% Sest: Solution found by the algorithm
% d : success index (d=1 is success, d = 0 is no convergence)
%
% Algorithm as described in "CoSaMP: Iterative signal recovery from
% incomplete and inaccurate samples" by Deanna Needell and Joel Tropp.
%
% This implementation was written by David Mary
%
% This script/program is released under the Commons Creative Licence
% with Attribution Non-commercial Share Alike (by-nc-sa)
% http://creativecommons.org/licenses/by-nc-sa/3.0/
% Short Disclaimer: this script is for educational purpose only.
% Longer Disclaimer see http://igorcarron.googlepages.com/disclaimer
% Initialization
Sest=zeros(size(Phi,2),1);
utrue = Sest;
v=u;
t=1; T2=[];
while t < 101
[k,z]=sort(abs(Phi'*v));k=flipud(k);z=flipud(z);
Omega=z(1:2*K);
T=sort(union(Omega,T2));phit=Phi(:,T);
% The next step is the one that can be improved with a Conjugate Gradient
% algorithm
b=abs(pinv(phit)*u);
[k3,z3]=sort((b));k3=flipud(k3);z3=flipud(z3);
Sest=zeros(size(utrue));
Sest(T(z3(1:K)))=abs(b(z3(1:K)));
[k2,z2]=sort(abs(Sest));k2=flipud(k2);z2=flipud(z2);
T2=z2(1:K);
v=u-Phi*Sest;
d=0;n2=norm(abs(v),'inf');
if n2 < tol1
d=1;t=1e10;
end
t=t+1;
end
d = 0
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