🚀 go-pugleaf

In article <ben.712345975@galaxy>, ben@ph.tn.tudelft.nl (Ben Verwer) writes:
|> jle@dam.brown.edu (Jon Elion) writes:
|> 
|> >Would be interested in from anyone who has experience with image
|> >filtering techniques for noise reduction that simultaneously ENHANCE
|> >edges.  Normally, these two operations are mutually exclusive (as
|> >noise reduction can tend to BLUR edges, not sharpen them).  There
|> >exist non-linear approaches that achieve this goal (although
|> >tending to be computationally very intensive).
|> 
|> >Application is x-ray fluoroscopy (noise is Poisson).  Goal is to reduce
|> >the Poisson noise while providing edge enhancement.
|> 
|> >Anecdotes, experience, pointers to articles, etc. would be appreciated.
|> 
|> How about: Edge preserving smoothing
|> Proc, 4th Int. Conf on Pattern Recognition
|> Nov 7-10 1978 Kyoto
|> Nagao & Matsuyama
|> 
|> 
|> Basically, compute variance and mean in 4 quadrants in the neighbourhood
|> around a point and take the mean of the quadrant with the lowest vaariance.
|> In TCL-Image on a Quadra: 1.9 seconds for a 256*256 image independent of
|> neighbourhoodsize.
|> 
|> Ben

Yep, probably a good technique but howcome "independent of neighbourhood
size". I don't know that Quadra system but I get size independence only
on a vectorizing machine like a cray by careful splitting up the summations
over rows and then columns (and then CPU time is peanuts).

For other filter techniques see:
du Buf, J.M.H. and Campbell, T.G. (1990) A quantitative evaluation of edge-preserving smoothing techniques. Signal Processing 21, pp 289-301.

Contains also methods which enhance edges.

Hans dB