EC-433 Digital Image Processing Lecture 6 Intensity Transformations and Spatial Filtering Dr. Arslan Shaukat Acknowledgement: Lecture slides material from Dr. Rehan Hafiz, Gonzalez and Woods
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EC-433 Digital Image Processing
Lecture 6
Intensity Transformations and
Spatial Filtering
Dr. Arslan Shaukat
Acknowledgement: Lecture slides material from
Dr. Rehan Hafiz, Gonzalez and Woods
Basic Intensity Transformations
Image negatives
Log transformations
Power-law
transformations
Image Negatives
Image with intensity level: [0, L-1]
s = (L – 1) - r
Enhancing white or gray detail embedded in dark regions of an
image; especially when black areas are dominant in size.
Image Negatives
Log Transformations
s = clog(1 + r)
– c = constant
– r greater than or equal to 0
Useful for low contrast
dark images
Log Transformations
Properties of log transformations
– For lower amplitudes of input image the range of gray levels is
expanded
– For higher amplitudes of input image the range of gray levels is
compressed
Application:
– This transformation is suitable for the case when the dynamic
range of a processed image far exceeds the capability of the
display device (e.g. display of the Fourier spectrum of an
image)
– Also called “dynamic-range compression/expansion”
Fourier spectrum with values of range
0 to 1.5 x 106
Log Transformations
The result of applying
log transformation
Power-Law (Gamma) Transformations
)],(.[),( yxIcyxg
s = cr
where c and are
positive constants
Used to adjust contrast of an image by either expanding
or compressing gray levels
– γ< 1, gray-level expansion
– γ> 1, gray-level compression
– If γ=1 & c=1, identity transformation (s = r)
More versatile as compared to logarithmic curve
Gamma Function
Power Law Transformations
Power-law Transformations: Example
Gray level
expansion is
desired
Power-law Transformations: Example
Gray level
compression is
desired
Gamma Correction
A variety of devices used for image capture, printing,
display respond according to a power law and need to be
corrected
Gamma (γ) correction
– The process used to correct the power-law response
phenomena
Example of gamma correction
– Cathode ray tube response is non linear (is a power function)
– To linearise the CRT response, pre-process the input image
before inputting it into the monitor by using transformation
s = cr1/γ
Gamma Correction
Piecewise-linear Transformation Functions
Contrast stretching
Intensity-level slicing
Bit-plane slicing
The principal advantage is that the form of piecewise
functions can be arbitrarily complex.
Contrast Stretching
Goal:
Increase the dynamic range of the gray levels for low
contrast images so that it spans the full intensity range
Low-contrast images can result from
– poor illumination
– lack of dynamic range in the imaging sensor
– wrong setting of a lens aperture during image acquisition
Contrast Stretching
The locations of (r1,s1) and (r2,s2) control the shape of
the transformation function.
– If r1 = s1 and r2 = s2 the transformation is a linear function
and produces no changes.
– If r1 = r2, s1 = 0 and s2 = L-1, the transformation becomes a
thresholding function that creates a binary image.
– Generally, r1≤ r2 and s1≤ s2 is assumed.
16
Contrast Stretching
Intensity-level Slicing
To highlight a specific range of gray levels in an image
(e.g. to enhance certain features).
One way is to display a high value for all gray levels in
the range of interest and a low value for all other gray
levels (binary image).
The second approach is to brighten the desired range of
gray levels but preserve the background and gray-level
tonalities in the image.
Intensity-level Slicing
Intensity-level Slicing
Bit-plane Slicing
To highlight the contribution made to the total image
appearance by specific bits.
– i.e. Assuming that each pixel is represented by 8 bits, the image
is composed of 8 1-bit planes.
– Plane 1 contains the least significant bit and plane 8 contains
the most significant bit.
– Useful for analyzing the relative importance played by each bit
of the image
– Only the higher order bits (top four) contain visually significant
data. The other bit planes contribute the more subtle details
– Plane 8 corresponds exactly with an image thresholded at gray
level 128
Bit-plane Slicing
Bit-plane Slicing
Image Reconstruction using Bit-planes
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