sparse representation for image prediction

Question

I m working on this project where i have implemented the dictionary as well as the OMP algorithm now i cant understand how to implement on lena image.i am providing my code over here.

%function [X_pr pp]=ompmod_pred2(D1,a1,sl,errorGoal)
%____________________________________________________
%prediction using (Aurelie method)
% D1=Dictionary;
%a1=image block of size (16X16);
%sl=sparsity label
%X_pr=predicted block
s=8;
%D=first 3:4 of D1
D=D1(1:3*s^2,:);
%D2=last 1:4 of D1
D2=D1(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(D);
E2 = errorGoal^2*n;
for s1=1:s1
maxNumCoef = s1;
A2(:,1)= zeros(size(D,2),size(X,2));
errorRes = X;     % new entry
for k=1:1:P,
    x=X(:,k);
    residual=x;
    indx = [];
    a = [];
%    currResNorm2 = sum(residual.^2);  % creates problem for complex vectors 
    currResNorm2 = residual'*residual;    %'
    j = 0;
    while currResNorm2>E2  & j < maxNumCoef,
        j = j+1;
        proj=D'*residual;    %'
        pos=find(abs(proj)==max(abs(proj)));
        pos=pos(1);
        indx(j)=pos;
        z1=pinv(D(:,indx(1:j)));
        a=pinv(D(:,indx(1:j)))*x;
        residual=x-D(:,indx(1:j))*a;
%        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)=a(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 different 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;

Here is my code for creating a dictionary.

function[D]=dict1(s)
%Generate DCT Dictionary of size (s^2 x s^2)
a=idct(eye(s));
for k1=1:s^2
    if rem(k1,s)==0
b(:,(k1-1)*s+1:k1*s)=a(:,ceil(k1/s))*a(:,1)';    %'
    else
b(:,(k1-1)*s+1:k1*s)=a(:,ceil(k1/s))*a(:,rem(k1,s))';    %'
    end
end
D1=im2col(b,[s/2 s/2],'distinct');
D=im2col(D1,[s^2/4 4],'distinct');
end

where s=16

please help me .