% Use nlfilter2 for neighborhood processing.
% nlfilter2 is not compatible with nlfilter.  The function in nlfilter
% works on an mxn block and must return a scalar.  The function in nlfilter2 
% works on an (mxn)x(MxN) matrix and must return a 1x(MxN) vector where M 
% and N are the number of rows and columns in the image. nlfilter calls its
% function MxN times (once for each pixel).  nlfilter2 calls its function
% just once.  nlfilter2 uses a lot more memory (it makes mxn copies of the
% original image) but is much faster. Depending on the type of processing
% anywhere from 10 to 1000 times faster.% 

% a_img = imread('testimg.png');
a_img = uint8(255*rand(250, 250));
[M N] = size(a_img);
figure(1);
imshow(a_img);

b_img = im2double(a_img);

% Use nlfilter for median processing
nmedian1 = @(A) median(A(:));
tic
d1_img = nlfilter(b_img, [3 3], nmedian1);
e1 = toc;

% Use nlfilter2 for median processing
% You don't need to define nmedian2.  You can just pass '@median' as the 
% function argument to nlfilter2.  I defined it to make sure that I was
% comparing apples to apples.
nmedian2 = @(A) median(A);
tic
d2_img = nlfilter2(b_img, [3 3], nmedian2);
e2 = toc;

figure(2)
printf('Sum of abs differences = %f\n', sum(sum(abs(d1_img-d2_img))));
printf('nlfilter2 is %f times faster than nlfilter1\n',e1/e2);
imshow(d2_img);