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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).

Well, this is not exactly what you're asking for, but your question
reminds me of a homomorphic filtering technique in [1] for
"simultaneous dynamic range reduction and contrast enhancement."  The
technique may be adaptable to your problem.  A figure demonstrating
the effect is reproduced in [2, pp. 183-187] and [3, pp. 213-218].

If the method *does* turn out to be useful to you, I'd appreciate
hearing about it.

[1] Stockham, T. G., Jr.  (1972).  "Image Processing in the Context of
a Visual Model."  Proc. IEEE, vol. 60, no. 7, pp. 828-842.

[2] Gonzalez, R. C., and Wintz, P. (1987).  Digital Image Processing,
2nd ed., Addison-Wesley.

[3] Gonzalez, R. C., and Woods, R. E. (1992).  Digital Image
Processing, Addison-Wesley.

-- 
Steve Eddins	
eddins@holst.eecs.uic.edu 	(312) 996-5771 		FAX: (312) 413-0024
University of Illinois at Chicago, EECS Dept., M/C 154, 1120 SEO Bldg,
Box 4348, Chicago, IL  60680

In article <1992Jul27.021555.24987@cs.brown.edu>, 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).
|> 
	I had good success with the modified trimmed mean (MTM) filter :

Y H Lee & S A Kassam - Generalized Median Filtering and Related Nonlinear
Filtering Techniques, IEEE Trans. Acoustics Speech & Signal Processing, vol. 33,
no. 3, June 1985, pp 672 - 683.

	This is compared with other, similar, techniques in :

Y-H Lee & S Tantaratana - Decision-Based Order Statistics, IEEE Trans. ASSP, 
vol. 38, no. 3, March 1990, pp 406 - 420.

	As originally formulated the filter is one dimensional (and this is the
form in which I used it), but the second paper shows results of its use in 2-D,
presumably by applying it in one direction and then the other.


								Philip J. Naylor.

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

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