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efficient kernel or masking algorithm ?

I need to apply a smoothing type kernel across an image, and calculate the
standard deviation of the pixels masked by this kernel.

ie. lets say I have a 128x128 image.  I apply a 3x3 kernel (or simply a
mask) starting at [0:2,0:2] and use these pixels to find the standard
deviation for the center pixel [1,1] based on its surrounding pixels, then
advance the kernel etc deriving a std deviation image essentially.
I can see myself doing this 'C' like with for loops but does something exist
for IDL to do it better or more efficiently ?