Download - 4-Image Enhancement
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Image Enhancement
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References
1. Gonzalez and Woods, Digital Image
Processing, 2nd Edition, Prentice Hall,
2002.
2. Jahne, Digital Image Processing, 5th
Edition, Springer 2002.
3. Jain, Fundamentals of Digital Image
Processing, Prentice Hall 1989
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Overview
Human perception (focus of this
discussion)
Machine perception (ocr)
Application specific
Heuristic based: result better than the
original image subjective assessment
Spatial vs frequency domain
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Spatial Domain
Based on the collection of pixels in the
image
Enhancement techniques will yield Noise reduction
Neighborhood smoothing
Highlighting of desired feature
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Spatial Domain Math Framework
Typically, spatial domain based enhancement involves:
g(x,y) = T [f(x,y)],where f = input image; g = output image; T = operator defined on f based on a neighborhood
of x,y. If the neighborhood is 1x1 pixel, then the output intensity is dependent on the current
intensity value of the pixel, and can be represented as
s = T( r )
where r and s are gray level values of f(x,y) and g(x,y) at location x,y. In such situations T is
a gray-level transformation function.
From [1]
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Examples of Gray level Transformation
Functions (Point Processing)
Contrast Stretching
Best if input is 0
outside a range ofvalues.
Thresholding:
Result is a
binary Image
From [1]
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Larger Neighborhoods
Objective determine g(x,y) based on
input intensity (gray level) values f(x,y) in
the neighborhood of x,y.
Mask processing or filtering
Each of the elements in the neighborhood has
an associated weight
g(x,y) depends on f(a,b)|a,b N(x,y)
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Basic Gray Level Transformations
Dark
Ligh
t
Dark
Light
From [1]
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Image Negatives
In this example, using the image negative, it is easier toanalyze the breast tissue.
From [1]
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Log transformations
s = c log (1 + r)
r >= 0; hence 1 + r > 0; log 0 = ?
Log transformations are useful, when there is a large dynamic
range for the input variable ( r ).
Range: 0 to 1.5*106 Range: 0 to 6.2
From [1]
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Power law transformation
Stretch higher
(lighter) gray levels
Stretch lower
(ldarker) gray levels
Many display devices (e.g.
CRT) respond like the power
law, i.e intensity voltage
relationship is power law based
gamma 1.8 to 2.5. The display
will tend to produce images
darker than intended. So the
display is distorted. Gamma
correction is used to correct for
this distortion.
From [1]
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Display distortion correction
1. Gamma correction
can also fix the
distortions in color.
2. More important with
the internet.
3. Many viewers, varietyof monitors.
4. Gamma of view
station is not known.
5. Preprocess using an
average gamma.
From [1]
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Power law contrast manipulation
(c) better than (b).
(d) Background is
better than (c) but
washed out effect.
From [1]
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Piece-wise Linear Transformation
Contrast Stretching
Mean gray level value
of the image
From [1]
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Gray level Slicing
From [1]
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Bit plane Slicing
From [1]
MSb (bit 7)
LSb (bit 0)
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Histograms
Histogram - frequency of occurrence of a gray levelvalue
Normalizing histograms with rest to the total number of
pixels converts these into probability density like function
Histogram processing yields robust image processingresults
Histograms are NOT unique
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Histograms for 4 images
From [1]
1. For high contrast, it is best to have a
larger range of gray level values.
2. If we could transform an image with a
resulting change in histogram, then
that may yield more contrast.
3. We need to study the rules fortransforming histograms, and study
the resulting impact on images.
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Histogram Equalization
From [1]
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Transformation Functions
From [1]
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Mapping for Histogram
Specification
From [1]
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Example of Histogram Specification
From [1]
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Continued
From [1]
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Localized Histogram Equalization
From [1]
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From [1]
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Histogram Stats
From [1]