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efficient kernel or masking algorithm ?
- Subject: efficient kernel or masking algorithm ?
- From: "Richard Tyc" <richt(at)sbrc.umanitoba.ca>
- Date: Wed, 29 Nov 2000 13:37:02 -0600
- Newsgroups: comp.lang.idl-pvwave
- Organization: The University of Manitoba
- Xref: news.doit.wisc.edu comp.lang.idl-pvwave:22361
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 ?