why each pixel of the predicted image is same?

I m working on prediction using orthogonal matching pursuit algorithm where at the output i m getting same pixel value through out the patch. i m providing the program.

D=Dictionary;
%a1=image block of size (16X16);
%sl=sparsity label
%X_pr=predicted block
a1=[162,162,162,161,162,157,163,161,166,162,162,160,155,163,160,155;162,162,162,161,162,157,163,161,166,162,162,160,155,163,160,155;162,162,162,161,162,157,163,161,166,162,162,160,155,163,160,155;162,162,162,161,162,157,163,161,166,162,162,160,155,163,160,155;162,162,162,161,162,157,163,161,166,162,162,160,155,163,160,155;164,164,158,155,161,159,159,160,161,160,155,159,154,154,156,154;160,160,163,158,160,162,159,156,159,163,158,163,155,155,156,152;159,159,155,157,158,159,156,157,159,162,162,160,153,153,153,151;155,155,158,158,159,160,157,157,163,157,159,160,159,154,156,157;155,155,157,158,155,154,155,157,161,155,155,157,157,157,157,152;156,156,156,160,156,155,155,152,159,159,157,157,159,154,158,154;156,156,156,159,159,155,150,148,160,159,158,158,156,155,157,158;158,158,157,156,157,153,159,156,161,158,160,157,156,153,156,171;157,157,157,157,160,157,156,156,159,155,154,158,156,155,154,160;158,158,159,155,155,158,158,156,157,156,151,160,157,156,156,155;158,158,159,157,155,158,157,154,158,158,158,159,155,156,154,171];
s4=16;
D=dict1(s4);
%D=first 3:4 of D1
s1=100;
s=8;
errorGoal=10;
D1=D(1:3*s^2,:);
%D2=last 1:4 of D1
D2=D(3*s^2+1:4*s^2,:);
%X_ac=block to be predicted
%X_ac=a1(9:16,9:16);
%X1=reshape a1 to column
Y1=im2col(a1,[s,s],'distinct');
X1=im2col(Y1,[s^2,4],'distinct');
%X=first 3:4 of X1
X=X1(1:3*s^2,1);
%X2=last 1:4 of X1
X2=X1(3*s^2+1:4*s^2,1);
%___________________________________
%A2=calculation of solution vector at 
%diffrent sparsity label using OMP
%___________________________________
[n,P]=size(X);
[n,K]=size(D1);
E2 = errorGoal^2*n;
for s1=1:s1
maxNumCoef = s1;
A2(:,1)= zeros(size(D1,2),size(X,2));
errorRes = X;     % new entry
for k=1:1:P,
    x=X(:,k);
    residual=x;
    indx = [];
    e = [];
%    currResNorm2 = sum(residual.^2);  % creates problem for complex vectors 
    currResNorm2 = residual'*residual;
    j = 0;
    while currResNorm2>E2  & j < maxNumCoef,
        j = j+1;
        proj=D1'*residual;
        pos=find(abs(proj)==max(abs(proj)));
        pos=pos(1);
        indx(j)=pos;
        z1=pinv(D1(:,indx(1:j)));
        e=pinv(D1(:,indx(1:j)))*x;
        residual=x-D1(:,indx(1:j))*e;
%        currResNorm2 = sum(residual.^2);
        currResNorm2 = residual'*residual;
   end;
   if (length(indx)>0)
       %_____________________________________
       l1=length(indx);
for k1=1:l1
for k2=1:K
    if k2==indx(1,k1)
        A1(k2,k1)=e(k1,1);
    else A1(k2,k1)=0;
    end
end
if l1==1
    A=A1;
else
A=sum(A1')';
end
       %_______________________________________

         errorRes(:,k)=residual;   % new entry
         end
     end;
  end
     A2=[A2 A];
  end
  %________________________________________________
  %X3=calculation of error at diffrent sparsity label
  l1=length(indx);
  for k1=1:l1
  X3(k1)=((D2*A2(:,k1+1))-X2)'*((D2*A2(:,k1+1))-X2);  
  end
  % %________________________________________________
  %pv=minimum error,pp=iteration 
  [pv pp]=min(X3);
  %X_pr=predicted block
  X_pr1=D2*A2(:,pp+1);
  X_pr=col2im(X_pr1,[8 8],[8 8],'distinct');
  %_________________________________________________
  return;
   disp(X_pr);
X_pr is the output where whole patch has same pixel value.help me out.