Intensity Transformation
Objective of this session
●Necessity of image enhancement
●Spatial domain operations
- Point processing
- Histogram based techniques
- Mark processing
Objective of Image Enhancement
●The objective of image enhancement is to
process an image so that the result is more
suitable than the original image for a specific
application.
●Image enhancement approaches fall into two
broad categories:
●Spatial domain
●Frequency domain
Spatial Domain Process
Neighborhood
Intensity Transformation function
The smallest possible
neighbourhood is of size 1x1.
S = T(r)
●Contrast stretching
If
T is in the form as shown in Figure(left), the result of
applying the transformation to every pixel in f to generate
the corresponding pixels in g would be to produce an
image of higher contrast than the original, by darkening
the intensity levels below k and brightening the levels
above k. I
●Thresholding function
Basic Gray Level Trasformation
Image Negatives: S = (L -1) -r
This type of processing is particularly suited for
enchancing white or gray detail embedded in
dark regions of an image, when the black areas
are dominant in size.
Example: Image Negatives
Log Transformation
● S = c log(1 +r)
● This transformation maps a
narrow range of low graylevel values in the input
image into a wider range of
output levels. Conversely,
higher values of input levels are
mapped to a narrower range in
the output.
•
The log function has the
important characteristic that
it compresses the dynamic
range of images with large
in pixel values
Example: Log Transformations
Power-Law Transformation
● A variety of devices used for
image capture, printing and
display respond according to
a power law.
● CRT devices, have an
intensity-to-voltage response
that is a power law function,
with exponent varying from
1.8 to 2.5.
Example: Gamma Transformations
Example: Gamma Transformations
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Contrast Stretching
●Low-contrast images can result from poor
illumination, lack of dynamic range in the
imaging sensor or even wrong setting of a
lens aperture during image acquisition.
●Piecewise Linear transformation function is
used for Contrast stretching .
●Contrast stretching expands the range of
intensity levels in an image so that it spans
the ideal full intensity range
Contrast Stretching
● The location of points (r1, s1)
and (r2, s2) control the shape of
the transformation function.
● If r1 = s1 and r2 = s2, the
transformation is a linear
function.
● If r1 = r2, s1 = 0 and s2 = L-1,
the transformation is a
thresholding function
● Intermediate values of (r1, s1)
and (r2, s2) produce various
degree of spread in the gray
levels of the output image.
Contrast Stretching
Intensity-Level Slicing
•
There are applications in which it is of interest to highlight a
specific range of intensities in an image. Some of these
applications include enhancing features in satellite imagery,
such as masses of water, and enhancing flaws in X-ray images.
The method, called intensity-level slicing.
•
This can be implemented several ways.
•
One approach is to display in one value (say, white) all the
values in the range of interest and in another (say, black) all
other intensities.
• The second approach, based on the transformation in Fig.
brightens (or darkens) the desired range of intensities, but leaves
all other intensity levels in the image unchanged.
Gray-level Slicing
** Highlighting
a specific rage of gray level in a image often is desired.
* Application includes enhancing flaws in X-ray images.