2. Image enhancement is the process of making
images more useful.
The reasons for doing this include:
Highlighting interesting detail in images
Removing noise from images
Making images more visually appealing
3. Methods of Enhancement
1. Contrast manipulation
Contrast stretching = expand the DN values beyond
their natural range to fill the 0-255 range.
2. Spatial feature manipulation
Refers to image texture.
Smooth areas have low spatial frequencies, gray values
change gradually.
Rough areas have high spatial frequencies and gray values
change abruptly.
4. Methods of Enhancement
3. Multi-image manipulation.
Two or more images combined
mathematically, commonly by ratios.
Used to develop green vegetative index images, e.g., the
NDVI.
6. Filters
Low-pass filter –
designed to emphasize larger, homogeneous areas of similar tone
and reduce smaller detail.
low-pass filters smooth the appearance of an image.
High-pass filters do the opposite –
sharpen the appearance of fine detail in an image.
Directional, or edge detection filters are designed to highlight
linear features, such as roads or field boundaries.
enhance features which are oriented in specific directions.
useful for detection of linear geologic structures.
13. Convolution
Spatial filtering is but one spatial application of the generic image processing
operation called convolution. Convolving an image involves the following procedures.
•A moving window is established that contains an array of coefficients or weighing
factors. Such arrays are referred to as operators or kernels , and they are normally an
odd number of pixels in size (eg. 3 x 3,5 x 5)
•The kernel is moved throughout the original image and the DN at the center of the
kernel in a second(convoluted) output image is obtained by multiplying each coefficient
in the kernel by the corresponding DN in the original image and adding all the resulting
products. This operation is performed for each pixel in the original image.
14. Convolution
The generic image processing operation
Spatial filter
convolution
Procedure
Establish a moving window (operators/kernels)
Moving the window throughout the original image
Example
(a) Kernel
Size: odd number of pixels (3x3, 5x5, 7x7, …)
Can have different weighting scheme (Gaussian
distribution, …)
(b) original image DN
(c) convolved image DN
Pixels around border cannot be convolved
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16. EDGE ENHANCEMENT
The purpose of edge enhancement is to highlight
fine detail in an image or to restore, at least partially,
detail that has been blurred (either in error or as a
consequence of a particular method of image
acquisition).
17. Edge enhancement
Typical procedures
Roughness
kernel size
Rough small
Smooth large
Add back a fraction of gray level to the
high frequency component image
High frequency exaggerate local
contrast but lose low frequency
brightness information
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23. DIRECTIONAL FIRST DIFFRENCING
It is another enhancement technique aimed at emphasizing edges in image data.
It is a procedure that systematically compares each pixel in an image to one of
its immediately adjacent neighbors and displays the difference in terms of the
gray levels of an output image.
This process is mathematically asking to determine the first derivative of gray
levels with respect to a given direction.
The direction used can be horizontal, vertical , or diagonal.
Determine the first derivative of gray levels with respect to a
given direction.
Normally add the display value median back to keep all positive
values.
24. Fourier analysis
Spatial domain
frequency domain
Fourier transform
Conceptual description
Fit a continuous function through the discrete DN values if they
were plotted along each row and column in an image
The “peaks and valleys” along any given row or column can be
described mathematically by a combination of sine and cosine
waves with various amplitudes, frequencies, and phases
Fourier spectrum
Low frequency
center
High frequency
outward
Vertical aligned features
horizontal components
Horizontal aligned features vertical components
