Matlab has a handy built-in function to do two-dimensional convolutions. These are useful for a variety of reasons: in any situation where you have a 2-D array, and you want to adjust each value in the array according to the values of its neighbors, a 2-D convolution can do this quickly and painlessly. It's a standard tool used, for example, in image processing: programs like Photoshop use 2-D convolutions to filter images in various ways, such as blurring or sharpening it.
Here's a related problem I encountered recently. Suppose, instead of adjusting each array value as a weighted sum of its neighbors, you want to use each array value to adjust all its neighbors. Specifically, you've got an array of mostly 1's, interspersed with the occasional 0, and you want to take each zero and surround it with 8 other 0's. One way to do this is to have a nested for-loop that checks if each element is equal to 0, and if so, sets its neighbors to 0. This is the brute force method. One annoying feature of this approach, however, is that you have to include special exceptions for any values along the edges: for example, if there's a 0 in the first row of your array, then it only has 6 neighbors to set equal to 0. Also, if the array is of any reasonably large size, nested for-loops take forever and a day to run. Anyway, here's a code snippet I wrote that avoids some of these issues:
A = ones(5);
A(1,1) = 0;
A(2,4) = 0;
pA = padarray(A,[1 1],1);
A1 = padarray(A,[2 2],1,'post');
A2 = padarray(padarray(A,[0 1],1),[2 0],1,'post');
A3 = padarray(padarray(A,[0 2],1,'pre'),[2 0],1,'post');
A4 = padarray(padarray(A,[1 0],1),[0 2],1,'post');
A5 = padarray(padarray(A,[1 0],1),[0 2],1,'pre');
A6 = padarray(padarray(A,[2 0],1,'pre'),[0 2],1,'post');
A7 = padarray(padarray(A,[0 1],1),[2 0],1,'pre');
A8 = padarray(A,[2 2],1,'pre');
zA = pA.*A1.*A2.*A3.*A4.*A5.*A6.*A7.*A8;
zA(1,:) = [];
zA(:,1) = [];
zA(6,:) = [];
zA(:,6) = [];
So, what this does is sort of like a convolution. It first 'pads' the array with 1's along all its edges, then creates 8 arrays that are shifted to every position that needs to be zeroed out. Then it multiplies all the arrays together (element-wise multiplication, of course, not matrix multiplication!). The final four lines just chop off the 1's that the array was padded with.
While this works fine, and is much faster than looping through the indices explicitly, I have a nagging question of whether this can in fact be done using a standard 2-D convolution/deconvolution operation... Hmm. In any case, in the off chance that someone will find this code useful, it's here!
UPDATE (2/20/11): As KSP points out in the comments, this is really just an interpolation. With this in mind, it is straightforward to rewrite this as a convolution:
A = ones(5);
A(1,1) = 0;
A(2,4) = 0;
F = ones(3);
zA = ~conv2(double(~A),F,'same');
(F is the convolution filter.) This does the same thing as the previous code snippet, but it runs about 3.5 times faster. Not sure how I didn't see that you could do it this way to begin with! D'oh.
