Contrast Enhancement:
What for
CE is between the imaging device output and the
display voltages
Get best perceived intensities to communicate
desired information
The choice of an intensity mapping function
must be made
That
is, to do contrast enhancement is not the
choice; to do it well is the choice
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In order to see
the dark areas,
the light looks
over exposed.
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Contrast Enhancement:
Two General Strategies
Optimize information transmission via intensity
mappings
Histogram
as probability
Information theoretic argument leads to uniform
probability distribution, so histogram flattening
Optimize contrast at spatial scales where most
important information on object lies
Smaller
scales are where object boundary info is
dominant; at yet smaller scales noise is dominant
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Contrast Enhancement:
Techniques
Global
vs. Local
Global
Intensity
mappings
Intensity
Windowing
Histogram Equalization
Achieving other histograms
Optimize
contrast at boundary
Scale
decomposition and then magnification of
components at appropriate scales (e.g., MUSICA)
Unsharp Masking
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Contrast Enhancement:
Techniques
Global
vs. Local
Locally adaptive
Intensity
mapping
Adaptive
Optimize
Histogram Equalization
contrast at boundary
Geometry-limited
diffusion
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Intensity Windowing
Dedicate the range of display intensities to a limited
window of recorded intensities.
Moves perceived object boundaries.
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Unsharp Masking
Inew = α · (Gsmall*I – Gbig*I) + Gbig*I
Adds detail to a background image.
Amplifies Mach bands.
MUSICA – Multiple level-of-detail images,
combination of which forms result.
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Unsharp Masking: Intuitive w/r to the
visual system
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Global Histogram Equalization
Theory: Information cleared up is maximized if the
image has maximum uncertainty, i.e. a flat
histogram
Intensities are mapped to their rank in the image.
Does not account for human contrast sensitivity,
which is local.
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Adaptive Histogram Equalization
Intensities are mapped to their rank in
the contextual region (window).
Enhances noise in smooth regions
Correction: limit the slope of the intensity mapping.
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Bilateral Filtering: VCD and form
system
Think: form system input to
diffusion process, and diffusion.
Blurs within boundary by using a
weighting function that is the
product of two Gaussians:
Gaussian with a spatial kernel: closer
pixels have higher weight.
Gaussian in the intensity domain:
higher weights for pixels with similar
intensities.
Perona and Malik, 2002
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Test Question:
Which row(s) show
local histogram
equalization and
which show the
global? How does
the difference
manifest itself on
the spine?
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Test Question:
a. The doctors would like to diagnose a renal disorder that
manifests itself in CT as a slight abnormality in a small
region of the kidney image.
1. What kind of techniques may be applicable: list them.
ans: contrast enhancement on a small scale. Local
techniques are windowing, adaptive histogram
equalization and derivatives.
2. If the abnormality is a matter of shape, why would
windowing be a bad idea?
ans: during windowing, isocontours of intensity move in
the image. This may distort the perceived shapes in the
image.
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Test Question:
b. The doctors wish to diagnose a disease that causes distant
large areas of the image to slightly change intensities with
respect to one another when they are usually identical.
Name a technique that may work to enhance this effect in
the image and why.
ans: windowing may work. The min and max of the
window could be set so that one area is very dark and the
other is very bright. The high contrast would be
noticeable to the doctor.
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That’s It! Thank You.
•Try it out: fspecial, imfilter, imshow,
imhist, histeq, conv2
•Matlab example.
s = double(imread(‘moon.tif'));
s = s/max(max(s));
figure, imshow(s, [min(min(s)) max(max(s))]);
h = fspecial('gaussian', [20 20], 4);
sb = imfilter(s, h);
figure, imshow(sb, [min(min(sb)) max(max(sb))]);
sb = sb/max(max(sb));
sd = s - sb;
figure, imshow(sd, [min(min(sd)) max(max(sd))]);
news = 4*sd + sb;
figure, imshow(news);
•midag.cs.unc.edu
•Pizer SM, Hemminger BM, Johnston, “Display of Two
Dimensional Images”, in Image-Processing Techniques for Tumor
Detection, edited by Strickland. 2002 Marcel Deckker, Basel,
Switzerland. ISBN 0-8247-0637-4.
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Matlab histogram equalization
s = double(imread(‘moon.tif”));
figure, imshow(s, [min(min(s)) max(max(s))]);
x = reshape(s, prod(size(s)),1);
[n,y] = hist(x,0:255);
n = n/sum(n);
cn = cumsum(n);
figure, plot(y,n,y,cn);
J = cn(s+1);
figure, imshow(J, [min(min(J)) max(max(J))]);
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