least mean square filter to reduce noise in image?

Question

I have a reference image and output image which is having lot of noise.I created a mask for a portion in both images.I wanna design a filter which when applied to this region,can be applied to whole region.i am using least mean square method to reduce noise.But each time the mean square keeps increasing.Any idea how to sort out this problem.I am using MAT LAB to do this.Here is my code.

    output=double(imread('obtained_output.jpg'));
    reference=double(imread('reference_output.jpg')); 
    [M,N]=size(output);
    upper_mask_obtained = output(1:100, 1:100);
    lower_mask_obtained=output(201:300,1:100);
    total_mask_obtained=[upper_mask_obtained;lower_mask_obtained];
    upper_mask_reference = reference(1:100, 1:100);
    lower_mask_reference=reference(201:300,1:100);
    total_mask_reference=[upper_mask_reference;lower_mask_reference];
           Ns=5;
          [a,b]=size(total_mask_reference);
           u=.000000001;
           W=ones(Ns,Ns);
          Y=zeros(Ns,Ns); 
          DD=zeros(Ns,Ns);
          error=zeros(M,N);
          e=zeros(Ns,Ns);
          error_mask=abs(total_mask_obtained-total_mask_reference);
          s= sum(sum(error_mask.^2));
          mean_square_error=(s/(a*b));

            while(mean_square_error>7)

            for m=1+Ns:200
             for n=1+Ns:100 

               for l=1:Ns
                 for k=1:Ns

                    Y(l,k)=total_mask_obtained(m-Ns+l-1,n-Ns+k-1);
                    DD(l,k)=total_mask_reference(m-Ns+l-1,n-Ns+k-1);     
                 end
                end 

            Z=conv2(Y,W,'same')/sum(sum(W));
            e=DD(3,3)-Z(3,3); 
            W=(W+u*e*Y);
            total_mask_obtained(m-Ns+2,n-Ns+2)=Z(3,3);
           end 
          end 

          error=total_mask_reference-total_mask_obtained;
          mean_square_error=sum(sum(error.^2))/(a*b); 

         end

         figure(2);
         final_output=(conv2(output,W,'same')/(sum(sum(W))));
         imshow(uint8(final_output));

Answer 1

I think your task could be well-accomplished by using Gaussian filter. It is already built-in in Matlab. http://www.mathworks.com/help/images/ref/fspecial.html Check this for your reference. Sorry I'm new in StackOverflow. So I can't add comments yet. Sorry if it is not answer to your question.