HDTenEightyP/NetNews huggingface dataset:
From: sjreeves@eng.auburn.edu (Stan Reeves)
Subject: Re: Image Restoration
Keywords: Signal-to-Noise Ratio, Entropy, Resolution
Organization: Auburn University Engineering
Date: Fri, 28 Aug 1992 13:37:50 GMT

In article <yi.714929332@fife.ecn.purdue.edu> yi@fife.ecn.purdue.edu (Juneho Yi) writes:
>sjreeves@eng.auburn.edu (Stan Reeves) writes:

[ stuff on objective measures for restoration quality ]

>>I'm assuming that the original poster is interested in objective measures
>>that have some correlation to the assessment a human observer would make.
>>Does this measure have any relationship to subjective evaluation?  What is
>>the point of normalizing with respect to surface area?
>
>By normalizing the volume by the surface area, the value of the measure 
>can be compared for different size of images.

This is usually done simply by dividing by the number of pixels in the
image.  Is there any advantage of dividing by surface area?  By surface
area, are you thinking of an image as a 2-D contour map?  If you just
mean height*width, then that's the same as dividing by the number of
pixels.

Also, what about "volume"?  This sounds like mean absolute error to me.

>The Mean-Square error is kinda L2 metric.
>Apparently, if MSE is used as a measure, where the slope is high,
>the difference value is emphasized more than in flat regions.
>I am saying that MSE measure is ** not invariant ** w.r.t. a coordinate system.
>The original poster was looking for methods to compare various algorithms on
>image restoration, wasn`t it ?

When you speak of a coordinate system, are you thinking of an image as a
signal in two dimensions or are you thinking of each pixel as a dimension
so that we have a signal in an MxN-dimensional vector space for an MxN 
image?  If you mean the second, then MSE *is invariant* with respect to
a coordinate transformation.  It also seems pretty clear that it's invariant
in the sense of a 2-D coordinate transformation.  Can you clarify?



--
Stan Reeves
Auburn University, Department of Electrical Engineering, Auburn, AL  36849
INTERNET: sjreeves@eng.auburn.edu
From: kube@cs.ucsd.edu (Paul Kube)
Newsgroups: sci.image.processing
Subject: Re: 2D Fourier Transforms, in English.
Date: 27 Aug 92 23:26:24 GMT
Article-I.D.: sdcc12.37352
Organization: CSE Dept., UC San Diego
Originator: kube@kokoro.ucsd.edu


In article <1992Aug27.215818.10407@waikato.ac.nz>, singlis@waikato.ac.nz writes:
|> Thanks to all who replied about FFT segmentation.
|> 
|> How is the 2D case explained? Is the 2D transform a sum of "planar" sin/cos
|> "waves", or is it more of a breakdown of the horizontal groups of vertical
|> transforms?

The two are equivalent, as you can see by looking at the equations.
E.g. define the relationship between the Fourier pair f(x,y), F(u,v) as

	F(u,v) = \int \int f(x,y) \exp (-2 \pi i (ux + vy) )  dx dy     (1)
	f(x,y) = \int \int F(u,v) \exp ( 2 \pi i (ux + vy) )  du dv     (2)

Reorder the integration in (1) to get

	F(u,v) = \int  [ \int f(x,y) \exp (-2\pi i vy) dy ]  \exp (-2\pi i ux) dx

which shows you can think of the 2D transform F(u,v) as the 1D "horizontal"
transform of the function that is the 1D "vertical" transform of f(x,y) for 
each x.

On the other hand, (2) shows that f(x,y) is the sum of functions of the
form \exp(2\pi i (ux + vy)), each weighted by F(u,v) du dv.  Each component is a 
plane complex exponential of frequency \sqrt(u^2 + v^2) and 
orientation \arctan (v/y).

	--Paul
Newsgroups: sci.image.processing
From: spz@specklec.mpifr-bonn.mpg.de (S.P.Zeidler)
Subject: Re: 2d dft phase info
Organization: Max-Planck-Institut f"ur Radioastronomie
Date: Wed, 2 Sep 1992 19:43:06 GMT

In <9209011949.AA19843@hp-and.an.hp.com> susanm@hp-and.an.hp.com (Susan Margulies) writes:

>Basically, I am trying to come up with a way of visualizing the
>phase information as a function of position.  I am already
>displaying the magnitude information by mapping transform
>coefficients into grey scale information.  But since it is the phase
>information which is necessary (and sufficient) for image
>reconstruction, I would prefer to be studying the phase information.
>(I am doing graduate thesis research on image compression.)

One thing that you might want to take into account is that the Fourier
transform of an ordinary image is hermitic, ie if you do a transform
AxB real image -> AxB complex Fourier transform, you got quite some
redundancies.
Doing instead AxB image -> -(A/2+1)xB phase and (A/2+1)xB modulus
will give you a nicely viewable (and displayable) Fourier transform.
Also pseudocolor is no mean idea to display intensities ;-)

regards,
	spz
-- 
        spz is S.Petra Zeidler  Copyright 1992 S.Petra Zeidler
spz@specklec.mpifr-bonn.mpg.de  
 MPI fuer Radioastronomie Bonn  
    Auf dem Huegel 69, Germany  reading prescription: cum kilo salis
Newsgroups: sci.image.processing
From: thomsen@spf.trw.com (Mark Thomsen)
Subject: The Opposite of Colorize is ...?
Organization: TRW Inc., Redondo Beach, CA
Date: Sun, 30 Aug 92 22:28:26 GMT

What is the appropriate term for converting a color image into an
achromatic image? To go from achromatic to color is "psuedocolor"
or "colorize". Candidate terms we came up with are "bleach",
"decolor", and "fade".

Mark R. Thomsen
Newsgroups: sci.image.processing
From: schultz@halley.est.3m.com (John C. Schultz)
Subject: Re: Image Segmentation
In-reply-to: ttcain@vangogh.lerc.nasa.gov's message of Fri, 28 Aug 1992 22:39:22 GMT
Organization: 3M Company, 3M Center, Minnesota, USA
Distribution: usa
Date: Fri, 28 Aug 92 18:15:12 GMT

In article <1992Aug28.223922.11107@eagle.lerc.nasa.gov> ttcain@vangogh.lerc.nasa.gov (Maureen Cain) writes:

   I have an image that was taken through a microscope of a lead sample.
...
   median filter.  I need to get a closed contour from the image as if I took a
   pencil and traced it off the image.  

	   Is there a way to segment the image using contrast and variance 
   stats rather than using and edge detector? Or a combination of both?  Any
   help would be appreciated.  Thanks

The traditional way, which even works sometimes :-), is to use difference
of gaussian (or DOG) filters.  The basic premise is to size two
lowpass operations such that subtracting the two filtered images
results in a bandpass image retaining the object with the desired
spatial dimensions.  The absolute and relative size of the bandpasses
is therefore important.   

A reasonably good approximation is to take the difference of two
box-car filters (rectangles). The math is not as nice as the gaussian
filters but the programming is fairly trivial.

After thresholding, morphological operations can then clean up the
resulting image.
--
   John C. Schultz   +1 (612) 733-4047   schultz@halley.serc.3m.com
   How to include the taste of Glendronach in a multi-media system?
Newsgroups: sci.image.processing
From: ksh@mayo.edu (Kerrie Holton)
Subject: Re: Fractal Dimension
Organization: Mayo Foundation, Rochester MN. Campus
Date: Fri, 4 Sep 92 16:27:16 GMT


--There are lots of ways to estimate the "fractal dimension" of an image.  Are you
interested in grayscale patterns, edges, ...?

Many of the articles I've read have been published in IEEE PAMI.  In 1984, Pentland and Peleg
wrote several.

Kerrie 
---------------------------------------------------------------------
	" On the seventh day God rested, and that's when all the
	trouble started."
	
		- Efraim Racker, "A new look at mechanisms in 
		bioenergetics"
---------------------------------------------------------------------

:-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-)
Kerrie S. Holton               Internet:   ksh@mayo.edu
Medical Sciences Blding 
Mayo Clinic/Foundation        
Rochester, MN 55905       
talk: (507)284-4115
fax:  (507)284-1428    
:-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-):-)

Newsgroups: sci.image.processing
From: peterf@zuben.csis.dit.csiro.au (Peter A Fletcher)
Subject: Re: 2d dft phase info
Organization: CSIRO Division of Information Technology
Date: Wed, 2 Sep 1992 01:06:17 GMT


> From: susanm@hp-and.an.hp.com (Susan Margulies)

>Basically, I am trying to come up with a way of visualizing the
>phase information as a function of position. ...

Hello!

    I was interested in the same problem, and have heard other people
mension that displaying the phase as colour was effective.

    I have tried this, and it can produce some very pretty pictures.
A good colour mapping seems to be

  0 degrees -> red
 90 degrees -> blue
180 degrees -> green
270 degrees -> yellow

    As well as producing pretty pictures, this mapping has some nice
perceptual properties: colours that are 180 degrees apart are also
complementary as far as the human visual system is concerned, because
colour information gets into the brain as a luminance, blue/yellow
balance and green/red balance (I think!).

    I've put a pretty picture generated by this method in the
anonymous ftp area on our machine here; the directory is
/anonymous@csis.dit.csiro.au:pub/fft , and the two files are fft.ras.Z
(an eight bit rasterfile with phase/amplitude colurmap) and
input.ras.Z, which was the input file used to generate the pretty
picture.

    Note that to actually produce the fft image here, it is actually
necessary to translate the input image so that its centre is on the
origin, not in the middle of the space; if you perform an fft on the
untranslated image, you get a phase modulation of 180 degrees per
pixel, which smears out the colours and makes them impossible to see.

    Unfortunately, the phase examined in this way in any real (as in
real-life) image looks like gobbledygook.

-peter
--
Peter Fletcher,            Internet : peterf@csis.dit.csiro.au
PhD Student                Physical : CSIRO Division of Information Technology,
Phone    : +61-6-2750914              ANU, Acton, Canberra ACT AUSTRALIA
-------------------------------------------------------------------------------
Newsgroups: sci.image.processing
From: bozo@world.std.com (Stephen P Blackstock)
Subject: Re: Please Tell Me About Highspeed Digital Camera
Organization: The World Public Access UNIX, Brookline, MA
Date: Wed, 26 Aug 1992 15:59:10 GMT

In article <1992Aug14.221309.4092@ttinews.tti.com> hollombe@polymath.tti.com (The Polymath) writes:
>In article <NK4V.92Aug11225334@poe.acc.virginia.edu> koshiya@virginia.edu writes:
>
>}I heard recently that there exists highspeed digital camera which can
>}capture 2k frame/s.  Is it true?  Please post any info on it.
>}Info on articles would be appreciated, too.
>

Leaf Systems makes a digital camera back which attaches to a 
Hasselblad camera body.  It transfers image data (in about 2 seconds) to
a Macintosh Quadra via SCSI.  Resolution is 2K x 2K x 14 bits per pixel, so
the amount of data transfered from camera to Mac is 8MB.  Software allows
windowing and gamma-mapping of 14-bit data to produce 8-bit images.

More information can be had by contacting a marketing representative at 
Leaf Systems, Inc. (508)460-8300.

-Stephen Blackstock
 Leaf Systems, Inc.
From: bmh@louvre.cs.unc.edu (Brad Hemminger)
Newsgroups: sci.image.processing
Subject: Re: 2d dft phase info
Summary: Hue and Luminance methods for visualizing two parameter data
Date: 3 Sep 92 18:20:40 GMT

In article <9209011949.AA19843@hp-and.an.hp.com>, susanm@hp-and.an.hp.com (Susan Margulies) writes:
...
> Basically, I am trying to come up with a way of visualizing the
> phase information as a function of position.  I am already
> displaying the magnitude information by mapping transform
> coefficients into grey scale information.  But since it is the phase
> --
> Susan E. Margulies       
> HP Imaging Systems       
> 508-681-2153 (work)      
> susanm@hp-and.an.hp.com  


A couple of folks have responded with suggestions regarding using hue and
luminance (or value if you talk HSV) to show the two parameters.  We have
been working on multiparameter visualizations as applied to medical image
display (for instance showing metabolic and anatomic information in a single
image as opposed to two separate images).  The primary method I have
investigated for doing this is similar to what the other folks have
suggested, that being, using hue to encode one dimension and luminance the
other dimension.  This can be fairly easily done on standard unix
workstations (with 8 bit color, or better yet deeper framebuffers).  The
following is a summary of advice based on our current (limited) experience.

1) Do the lower frequencey signal with hue and the higher frequency signal with
luminance (humans don't perceive high frequency hue changes very
quantatively).  

2) Don't use low level luminance values (because you can't distinguish hues at
near black)

3) Provide interactive control.  For instance we allow the users to threshold
from 0-100% the hue mapping and 0-100% of the luminance mapping.  This
allows them to "fade" either dimension in and out.

4) A hue range from Red to Green works fairly well for a lot of visual
system reasons.  Avoid crossing over several different major colors (ie. no
rainbow hue attempts).  We are fairly picky on our choices (I use a
perceptually linear greyscale base on the luminance side, and at each a
luminance scale step a peceptually linear hue scale between two hues on the
hue side.  Note that you want to be careful in your hue choice offerings and
to offer some altervatives (as we were reminded by a couple of our RG
"colorblind" observers).

-Brad Hemminger
bmh@rad.unc.edu
Dept of Radiology
Medical Image Display Research Lab
University of North Carolina at Chapel Hill
Newsgroups: sci.image.processing,sci.astro
From: pgf@space.mit.edu (Peter G. Ford)
Subject: Re: image processing
Keywords: radar, compression, signal-processing
Reply-To: pgf@space.mit.edu
Organization: MIT Center for Space Research
Date: Wed, 2 Sep 1992 02:53:20 GMT

In article <simon_wing-010992180153@wings-mac.jhuapl.edu> simon_wing@spacemail.jhuapl.edu (simon ) writes:
>Is there anybody there who has done compression with radar data ?

You must be more specific: "radar data" can mean anything from the raw
signal to SAR range-Doppler images, quad-polarization vector images,
cartographic maps, altimeter profiles, etc. etc. Each can usually be
compressed if you're willing to spend CPU time or sacrifice resolution,
or both.

A raw SAR signal can usually be compressed since its SNR is greatly
increased during subsequent processing (range and azimuth compression).
The Magellan radar uses a 4:1 compression based on a "block floating-
point quantization" algorithm -- data sampled as 8-bit numbers are
split into 8-bit binary exponents and 2-bit mantissae; since the
exponents are expected to vary more slowly than the mantissae, blocks
of data are normalized to an average exponent.  Although this appears
to reduce the dynamic range of the resulting imaged from 256:1 to 4:1,
this is subsequently restored by the averaging that takes place during
range compression and multi-look summation.

4-polarization radar images, i.e. those that combine all senses of
transmit and receive polarization, are usually compressed into 10-
element symmetric Stokes matrix pixels, and then compressed as if they
were simple images.

It is quite difficult to devise an automatic method of compressing
simple images of radar backscatter cross-section. Since they are
usually computer-generated, their pixel dimension and DN range are
likely to be chosen to maximize information content. Secondly, the
dominant noise source is usually "speckle" and is proportional to the
local image intensity. If an 8-bit image appears "sharp" and "gritty"
to the eye, you probably won't get much reduction in size from a 
lossless algorithm such as Huffman or Lempel-Ziv. If you are prepared
to take the loss, run the image through a small median filter and then
use Lempel-Ziv -- but make sure that you aren't throwing away anything
important (e.g. the image of a corner-reflector that you'll need to
calibrate the image intensity!)

This same discussion applies equally to other coherent-source images,
e.g. sonar. Two of the largest imaging datasets currently published on
CD-ROM, the Magellan Venus mapping project and the Gloria side-scan
sonar ocean floor survey, have both elected not to compress their
products.

I'd be very interested to hear from anyone who is working on this
problem.

Regards,
Peter Ford
MIT and Magellan Project
Newsgroups: sci.image.processing
From: paul@monet.uwaterloo.ca (Paul Sommers)
Subject: Re: Locations for test images ?
Keywords: high res images, test images, 24 bit color
Organization: University of Waterloo
Date: Thu, 27 Aug 1992 15:53:15 GMT

In response to people looking for 24 bit colour images:

I found a good site to be:

ftp.ipl.rpi.edu:/pub/image/still/usc/color

They have images such as lenna, peppers, f16 etc.

They are in Sun raster format, but this can be converted
to ppm using the ppm tool rasttoppm.

-Paul Sommers
 paul@monet.uwaterloo.ca

-- 
Paul Sommers               | "Television.  Drug of the nation.
paul@monet.uwaterloo.ca    |  Breeding ignorance and feeding radiation"
University of Waterloo     | 
Waterloo, Ontario, CANADA  |    -Terminal City Ricochet
From: susanm@hp-and.an.hp.com (Susan Margulies)
Newsgroups: sci.image.processing
Subject: 2d dft phase info
Date: 1 Sep 1992 14:50:40 -0500
Organization: UTexas Mail-to-News Gateway
NNTP-Posting-Host: cs.utexas.edu

In recent posts I have seen some discussion about the phase
information which results from a two-dimensional discrete Fourier
Transform.  I am new to 2D Signal and Image Processing, and as such
have my own question to throw into the discussion.

Basically, I am trying to come up with a way of visualizing the
phase information as a function of position.  I am already
displaying the magnitude information by mapping transform
coefficients into grey scale information.  But since it is the phase
information which is necessary (and sufficient) for image
reconstruction, I would prefer to be studying the phase information.
(I am doing graduate thesis research on image compression.)

Any suggestions would be greatly appreciated, even if the advice is
to look at the phase information separately in each dimension!

Thanks,
Susan

--
Susan E. Margulies       
HP Imaging Systems       
508-681-2153 (work)      
susanm@hp-and.an.hp.com
From: cote@cs.ucsd.edu (Brad Cote)
Newsgroups: sci.image.processing
Subject: Re: Image Segmentation
Date: 29 Aug 92 00:32:47 GMT
Distribution: usa

ttcain@vangogh.lerc.nasa.gov (Maureen Cain) writes:


>	Is there a way to segment the image using contrast and variance 
>stats rather than using and edge detector? Or a combination of both?  Any
>help would be appreciated.  Thanks

>Maureen Cain

This might be helpfull:

"Image Segmentation by Succesive Background Extraction," by
A. Rodriguez and O. Mitchell, Pattern Recognition V24 #5
pp 409-420, 1991.

				Brad Cote'
				University of California, San Diego
				STARE Project
From: damour@rocket.sanders.lockheed.com (Kevin Damour)
Newsgroups: sci.image.processing
Subject: Locations for test images ?
Keywords: high res images, test images, 24 bit color
Date: 26 Aug 92 20:03:56 GMT
Organization: Lockheed Sanders, Inc.


    Hello,

	   I need to compare the output from several color sublimation
printers.  I do not know if this is a FAQ but...

	  Are there site which have 24 bit color images of full page
high resolution test images (maybe 4k by 4k pixels )? I was hoping for
images with bright colors, areas with continuous shading, and some
fine sharp boundary lines.

	  Thank you for any advice,
		  Kevin


        -----------------------------------------------------
Dr. Kevin T. Damour                              Lockheed Sanders Inc.
Principal Electrical Engineer                    PO Box 868, MER24-1583C
Signal Processing Center of Technology           Nashua, NH 03061-0868

  (usenet)      ...!uunet!rocket!damour            voice (603) 885-9122
  (internet)    <damour@rocket.sanders.com>        fax   (603) 885-0631
Newsgroups: sci.image.processing
From: mark@fenris.albany.edu (Mark Steinberger)
Subject: Re: Fractal Dimension
Organization: State University of New York at Albany
Date: Thu, 3 Sep 92 01:52:18 GMT

In article <1992Aug31.145423.8945@eplrx7.es.duPont.com> cristy@eplrx7.es.duPont.com (Cristy) writes:
>I am looking for source code that can compute the fractal dimension of
>an image.  Input is a 512x512 gray scale image.

How do you create such an image that isn't two-dimensional? The image,
after all, isn't the same as the mathematical data that generates it.

--Mark
Newsgroups: sci.image.processing
From: thomsen@spf.trw.com (Mark Thomsen)
Subject: Re: 2d dft phase info
Organization: TRW Inc., Redondo Beach, CA
Date: Wed, 2 Sep 92 03:05:26 GMT

Susan Margulies writes
> Basically, I am trying to come up with a way of visualizing the
> phase information as a function of position.  I am already
> displaying the magnitude information by mapping transform
> coefficients into grey scale information.

Map phase into color, or specifically hue. That is, map magnitude into L,
map phase into H, set S to 1.0 (or something close) and then convert
from HSL into RGB.

We did this and it sometimes worked. And sometimes didn't. Strongly
depends upon the sort of structures you are looking for.

Another to try is map magnitude into red and map phase into green. Some
shape information might come out better. Please note that the phase
map is circular to linear --- provide a rotation knob to alter the map
interactively to shift around the discontinuous point.

Good luck.

Mark R. Thomsen
Newsgroups: sci.image.processing
From: heuring@riker.cs.colorado.edu (Vincent Heuring)
Subject: Image Synthesis, Rendering and Display Algs
Organization: University of Colorado, Boulder
Date: Fri, 4 Sep 1992 18:24:27 GMT


I need references to texts that describe the algorithms for image
synthesis, rendering, and display. Entry level and advanced would
be appreciated!


-- 
---
 Vincent Heuring   Dep't of Electrical & Computer Engineering
 University of Colorado at Boulder      Boulder CO 80309-0425
 heuring@cs.Colorado.EDU   o) 303-492-8751    h) 303-449-8868
Newsgroups: sci.image.processing
From: mlelstv@speckled.mpifr-bonn.mpg.de (Michael van Elst)
Subject: Re: image processing loops: a c programming
Organization: Max-Planck-Institut f"ur Radioastronomie
Date: Wed, 26 Aug 1992 18:14:24 GMT

In <DAVIS.92Aug14222649@pacific.mps.ohio-state.edu> davis@pacific.mps.ohio-state.edu ("John E. Davis") writes:
>What about:  memcpy(bp, ap, MAX);  It will beat the above loop every time.

Not so sure. If the memcpy isn't optimized it will just do the same
plus the function call overhead.
And for any other type than char the for loop might even be better than
a dumb memcpy.

Regards,
-- 
Michael van Elst
UUCP:     universe!local-cluster!milky-way!sol!earth!uunet!unido!mpirbn!p554mve
Internet: p554mve@mpirbn.mpifr-bonn.mpg.de
                                "A potential Snark may lurk in every tree."
Newsgroups: sci.image.processing,comp.compression
From: keith@koala.aisg.com (Keith B. Kee {AISG} )
Subject: Re: JPEG-in-TIFF
Reply-To: keith@koala.aisg.com (Keith B. Kee {AISG} )
Organization: Zman
Date: Wed, 26 Aug 92 16:39:40 GMT

Please let me know too. I am interested.
Newsgroups: sci.image.processing