IMAGE ENHANCEMENT TECHNIQUES
ABSTRACT
Image
Processing is a technique to enhance raw images received from
cameras/sensors placed on satellites or air crafts or pictures taken in normal
day to day life various applications such as remote sensing, biomedical,
industrial, forensic sciences, etc. Some
times the images from various sensors may lack in contrast and may have
different types of noises. In order to
get the required details from images, they have to be enhanced using digital
image processing techniques. With the
availability of high-speed
computers and other peripherals, the usage of these techniques has become more.
This
package is useful for biomedical, graphic arts, industrial applications,
etc. It was developed in C++ language
under MS DOS environment. It has
different enhancement techniques like histogram based enhancements, filters in
spatial domain such as low pass, high pass, median, mean, sobel, lee filters,
display of histogram and statistics, display of images in B/W mode, etc.
CONTENTS
1. INTRODUCTION
(I)
IMAGE
PROCESSING
(II)
METHODS OF
IMAGE PROCESSING
(III)
APPLICATIONS
OF IMAGE PROCESSING
2. IMAGE ENHANCEMENT TECHNIQUES
(I)
STATISTICS
(II)
ADDITION
OR SUBTRACTION
(III)
HISTOGRAM
STRETCHING
(IV)
LOGARITHMIC
STRETCHING
(V)
MEDIAN
FILTER
(VI)
SOBEL
OPERATOR
(VII)
LOW PASS
FILTER
(VIII)
HIGH PASS
FILTER
(IX)
LEE FILTER
(X)
COPY
(XI)
MIRROR
(XII)
MERGE
(XIII)
HISTOGRAM
GRAY LEVEL GRAPH
3. RESULTS AND CONCLUSION
4. ANNEXURE - DIGITAL
IMAGE PROCESSING
(I)
DIGITAL
IMAGE REPRESENTATION
(II)
IMAGE
MODEL
(III)
FUNDAMENTAL
STEPS IN DIGITAL IMAGE PROCESSING
(IV)
IDEA ABOUT
IMAGE ENHANCEMENT TECHNIQUES
(V)
THE GRAY
LEVEL HISTOGRAM
(VI)
USES OF
HISTOGRAM
1. INTRODUCTION
(I) IMAGE PROCESSING
Images form
an overwhelming part of our experiences from birth. Before we can define image processing, we
must agree upon a definition of the word image.
“An
image is a representation, likeness, or limitation of an object or thing, a
vivid or graphic description, something introduced to represent something
else.”
For
example, a photograph of Abraham Lincoln, for instance, is a representation of
an American President.
An
image contains descriptive information about the object it represents.
Images can be classified into several types.
Consider
the set of all objects, the images form a subset. The physical images are distributions of
measurable physical properties. Optical
images are spatial distributions of light intensity. Example of non-visible physical images are
temperature, pressure, elevation and population density maps.
The
black and white image has one value of brightness at each point, the color
image has three values of brightness, one each in red, green, and blue. The three values represent intensity in
different optical spectra which the eye perceives as different colors.
Images
are used in various applications such as Remote Sensing, Bio-medical Engineering, Forensic
Sciences, Astronomy, Non-destructive testing, etc. These images can be obtained from the data
collected from various Satellites, Scanners, etc. In all these applications image processing is
required. Image processing in its
general form, pertains to the alteration and analysis of pictorial information.
Image
processing is defined as the various operations that can be applied to image
data. Activities in image processing
mean dealing with image acquisition devices (cameras, scanners), image
interface sub-systems (frame grabbers), digital computers and software for
processing.
Image
processing techniques are used to improve the visual appearance of an image, or
to convert the image to a form better suited for analysis by a human or a
machine.
In
industries, various products have to be checked for quality before
dispatching. The image processing
techniques are useful to find out the minor defects in the products.
(II) METHODS OF IMAGE PROCESSING
Functionally, three techniques are involved in
implementing image processing. They are,
A) Optical image processing
B) Analog image processing
C) Digital image processing
A) OPTICAL IMAGE PROCESSING :
In
this processing, it uses an arrangement of optics to carry out the process.
Examples are:
- Eyeglasses are
a form of optical image processing.
- Another important form of optical image processing is found in the photographic dark room. For years photographers have enhanced, manipulated and abstracted images from one form to another. The objective is always being to produce a more favorable image.
It
refers to the alteration of the image through electrical means. The most common example of this is television
image.
The
television signal is a voltage level which varies in amplitude to represent
brightness throughout the image. By
electrically altering the signal, we correspondingly alter the final displayed
image appearance. The brightness and
contrast controls on a TV set serve to adjust the amplitude and reference of
the video signal, resulting in the brightening, darkening and alteration of the
brightness range of the displayed image.
C) DIGITAL IMAGE PROCESSING :
“Digital
image processing is subjecting numerical representations of objects to a series
of operations in order to obtain a desired result. It starts with one image and produces a
modified version of the same. It is
therefore a process that takes an image into an image.”
Digital
image processing is a process that takes an image into an image.
Digital
image processing is a form of image processing brought on by the advent of the
digital computer. This form provides
flexibility and power for applications.
(III)
APPLICATIONS OF IMAGE PROCESSING
- Remote sensing
for natural resource mapping.( Mineral, Water,
- Remote sensing for prediction of natural phenomena( Weather, Floods, Monsoons )
- Image storage and analysis for medical diagnosis( X-ray imaging, Ultra-sound, CT scan, MRI, Orthopaedics, Cytoanalysis, Video-microscopy cosmetology )
- Machine vision for industrial automation and quality assurance
- Astronomy
- Military equipment
- Security and access control through finger-print and facial image databases
- Video information capture and printing in Desk-Top-Publication (DTP)
- Special effects and animation
- Picture communication using data compression ( HDTV, Video-phone, Tele-conferencing )
2. IMAGE
ENHANCEMENT TECHNIQUES
IMAGE ENHANCEMENT TECHNIQUES
These
are a group of operations that improve the detect ability of targets or
categories. These operations include
contrast improvement, edge enhancement, spatial filtering, noise suppression,
image smoothing and image sharpening.
These are broadly divided into two categories. They are
i)
Point operations
ii)
Local operations
A
POINT OPERATION is one in
which the output pixel value depends only on the value of the corresponding input
pixel. Point operations are sometimes
called Contrast manipulation or stretching.
Addition or Subtraction, Logarithmic stretching, Histogram based
enhancements fall into this category.
A
LOCAL OPERATION is one in
which the output pixel value depends on the pixel values in a neighborhood of
the corresponding input point. Median
filter, Sobel operator, Low pass filter, High pass filter, Lee filter into this
category.
I
developed software in C++ language under MS DOS*
environment. The programs are integrated
as menu. The executable name in
MENU.EXE. As the menu is executed the
following menu appears on the screen.
The detailed description is given below.
* *
* IMAGE ENHANCEMENT TECHNIQUES *
* *
*********************************************************************
* *
*
1 : STATISTICS *
*
2 : ADDITION OR SUBTRACTION *
*
3 : HISTOGRAM STRETCHING *
*
4 : LOGARITHMIC STRETCHING *
*
5 : MEDIAN FILTER *
*
6 : SOBEL OPERATOR *
*
7 : LOW PASS FILTER *
*
8 : HIGH PASS FILTER *
*
9 : LEE FILTER *
* 10 : COPY *
* 11 : MIRROR *
* 12 : MERGE *
* 13 : HISTOGRAM GRAPH *
* 14
: DISPLAY THE IMAGE *
* 15
: EXIT *
* *
********************************************************************
(I) STATISTICS
In
the image processing, before doing any enhancements to any image file, it is
required to know various parameters regarding the image file. The parameters are minimum, maximum, mean gray values,
variance, and standard deviation histogram.
The software calculates the above parameters and prints the information.
DESCRIPTION :
The software reads the data from input image
file and calculates minimum, maximum, mean gray values, variance, and standard
deviation for all pixels.
Sum
of squares – (Number of pixels X Mean)
Variance
= --------------------------------------------------
Number
of pixels - 1
Standard Deviation =
SQRT(Variance)
(II) ADDITION OR SUBTRACTION
The
program is useful for addition or subtraction.
This is useful to increase or decrease the intensity of a particular
image by the required amount.
DESCRIPTION :
The
inputs are input image file name, output image file name and the constant value
‘K’. For the addition operation,
‘I’ value is two. For the
subtraction operation, ‘I’ value is zero. The input pixel value is ‘A’. The output pixel value ‘B’ is computed
using the formula,
B =
A + ( I – 1 )
* K
The
above modification of pixel value will be done for the entire input image file
and will be written in to the output data file.
(III) HISTOGRAM STRETCHING
The
histogram of any image file will tells us the quality of the data in terms of
contrast and brightness. If the
histogram plot occupies the lower range of grey scale, i.e., less than 100 gray
values, the image will be dark. Most of
the details may not get. The histogram
stretch program stretches the entire image gray values between 0 to 255. So that, image will have good contrast.
DESCRIPTION :
The program
first calculates the minimum and the maximum pixel values in the input
image. Then the output pixel will be
modified as per the following formula,
X - X1
Y =
-------- * 255
X2 – X1
Where X1 = Minimum
pixel value in the input image
X2 = Maximum
pixel value in the input image
X = Input
pixel value
Y = Output
pixel value
The
above modification of pixel value will be done for the entire input image file
and will be written in to the output data file.
(IV) LOGARITHMIC STRETCHING
The
transformation expands the contrast of small gray values and compress the large
values. It also transforms
multiplicative noise to additive noise which can be easily removed. It makes low contrast details more visible by
enhancing low contrast edges.
DESCRIPTION :
The program firstly transforms the input image
into log image using the following function.
Y
= log
( X )
Where
Y = Output pixel
value
X = Input
pixel value
Then
the software calculates the minimum and maximum log values and stretches the
log values between 0 to 255 and writes into the output data file.
(V) MEDIAN FILTER
The
type of filter is especially useful to remove salt and pepper noise, speckle
noise from the images. These types of
noises belong to high frequency components.
Low pass filter removes these types of noises and the image becomes
smooth but blurred. But median filter
removes noise and image becomes smooth without any blur.
DESCRIPTION :
This
software works on 3X3 window basis. It
reads 9 pixels and finds the mean value.
Then the central pixel is replaced with the median value. Firstly, software reads 3 lines of data into
a buffer. 3X3 window operation will be
executed with single pixel stepwise throughout 3 lines of data. The resultant line of data will be written on
to the output file. The software reads
another line of data in place of first line and above repeats till the end of
data.
(VI) SOBEL OPERATOR
The
sobel operator is one of the edge enhancement operators. It works perfectly on a threshold
images. In order to get a binary image
threshold is used. This operator finds
an important role in image processing of industrial and medical applications.
DESCRIPTION :
It
works on 3X3 window basis. Let the 3X3
window of pixels appears as follows.
X11 X12 X13
X21 X22 X23
X31 X32 X33
The
central pixel value is modified as follows.
Central
pixel value = SQRT( X2 + Y2 )
Where
X = ( X13 + 2*X23 + X33 ) – (X11 + 2*X21 + X31)
Y = (
X11 + 2*X12 + X13 ) – ( X31 + 2*X32 + X33)
The above
procedure is repeated for three lines of data with single step increment
(window movement). The software reads
another line of data in place of first line and the above process repeats till
the end of data.
(VII) LOW PASS FILTER
Images
can contain random noise superimposed on the pixel brightness values. The noise may be generated in the transducers
which acquire the image data and during the transmission. This is also called ‘salt and pepper noise’. It can be removed by the process of low pass
filtering or smoothing but at the expense of some high frequency information of
the image. The filtering is performed by
means of 3X3 or 5X5 window.
DESCRIPTION :
The
software works on 3X3 or 5X5 window basis.
It reads 3 or 5 lines of data firstly, then it reads 3X3 or 5X5 window
of pixels. The pixels are multiplied by
the selected mask weights and the resultant value is replaced with the central
pixel value. The process repeats with
single pixel stepwise throughout the 3 or 5 lines of data. The resultant data (line) will be written to
the output data file. Then another line
of data will be read in place of first line and the above process repeats till
the end of data.
LPF3
(3X3 Window)
Enter
the mask number :
(1) .25 .5 .25
.5 1 .5
.25 .5 .25
(2) .1 .1 .1
.1 .1 .1
.1 .1 .1
(3) .1 .1 .1
.1 .2 .1
.1 .1 .1
LPF5 (5X5 Window)
Enter the mask number :
(1) .1 .1 .1 .1 .1
.1 .1 .1 .1 .1
.1 .1 .2 .1 .1
.1 .1 .1 .1 .1
.1 .1 .1 .1 .1
(2) .1 .1 .1 .1 .1
.1 .1 .1 .1 .1
.1 .1 .1 .1 .1
.1 .1 .1 .1 .1
.1 .1 .1 .1 .1
(VIII) HIGH PASS FILTER
In any images high frequency contents and low frequency contents are available. The high frequency contents are associated with frequent changes of brightness with position. Edges, lines and some types of noise are examples of high frequency data. This type of data plays an important role in remote sensing data. The high pass filter allows high frequency contents and filters low frequency contents. Then the extraction of information from edges becomes simple. The filtering is performed by means of 3X3 or 5X5 window.
DESCRIPTION :
The
software works on 3X3 or 5X5 window basis.
It reads 3 or 5 lines of data firstly, then it reads 3X3 or 5X5 window
of pixels. The pixels are multiplied by
the selected mask weights and the resultant value is replaced with the central
pixel value. The process repeats with
single pixel stepwise throughout the 3 or 5 lines of data. The resultant data (line) will be written to
the output data file. Then another line
of data will be read in place of first line and the above process repeats till
the end of data.
HPF3
(3X3 Window)
Enter the mask number :
(1)
1 -2 1
-2 5 -2
1 -2 1
(2) -1 -1 -1
-1 9 -1
-1 -1 -1
(3) 0 -1 0
-1 4 -1
0 -1 0
HPF5
(5X5 Window)
Enter the mask number :
(1) 0
0 -1
0 0
0 -1 0 -1 0
-1 0 10 0 -1
0 -1 0 -1 0
0
0 -1
0 0
(2) -1 -1 -1 -1 -1
-1 -1 -1 -1 -1
-1 -1 25 -1 -1
-1 -1 -1 -1 -1
-1 -1 .1 -1 -1
(IX) LEE
FILTER
Computational
techniques involving contrast enhancement and noise filtering on
two-dimensional image arrays are developed based on their local mean and
variance. Lee filter is a type of
enhancement based on local statistics especially mean value. It enhances the edges in a given image. It is most effective for high-resolution data
like IRS, LANDSAT and SPOT data sets. It
works on 5X5 window basis.
DESCRIPTION :
The
Lee filter works on 5X5 window basis.
The software firstly reads 5 lines of input data. Then it reads 5X5 window pixels, i.e., 25
pixels. Then the mean value is
calculated for 25 pixels. The resultant
pixel is calculated using the formula.
X’ = mean
+ 2 * ( X – mean )
Where
X = Input pixel
value
X’ = Output
pixel value
The central
pixel is replaced with the output pixel value.
The above process is repeated till the end of data.
(X) COPY
The
images taken from various X-ray photographs or tomographs or some other form
are of different sizes. Sometimes the
sizes are of order 2000 pixels X 2000 lines or so. But the necessary area in the images may be
small. This program will copy the
required portion of the images into another file for further processing.
DESCRIPTION :
The
inputs for the program are input and output file names, starting pixel and line
and the ending pixel and line of input image.
According to the above information, the required portion from the input
file will be copied into the output file.
(XI) MIRROR
The
digital images are obtained thru satellites and using transducers such as
digitizers. Drum scanners, frame
grabbers, etc., comes under the category of digitizers. Sometimes if a map or X-ray photograph is
digitized with scanners, mirror image may be obtained. So, before doing any image processing, the mirror
image has to be flipped to the right image.
This program will do the above task.
DESCRIPTION :
The
program reads one line of data from the input data file. It flips the mirror image data to the right
image data and writes into the output data file. The process continues till the end of input
data file.
(XII) MERGE
In
image processing, the processed data will be in different file. If we want to see the input and output data
files, they have to be loaded separately.
They cannot be seen at a single time.
This program combines various input files into a single file based on
the position of given co-ordinates. If
there are two images of size 512X512, the output file will have a size of
512X1024. Here the two images are kept
side by side.
DESCRIPTION :
The
inputs for the program are two input file names and one output file name, sizes
of two input file names in terms of pixels and lines. According to the above information, the two
input images are merged and placed in the output file.
(XIII) HISTOGRAM GRAY LEVEL GRAPH
In
the image processing, before doing any enhancements to any image file, it is
required to know various parameters regarding the image file. The parameters are minimum, maximum, mean
gray values, variance, standard deviation, histogram values and histogram
plots. The software calculates the above
parameters and prints the information and histogram plot on the PC terminal.
DESCRIPTION :
The
software reads the input image file data and calculates minimum, maximum, mean
gray values, standard deviation, mean, histogram values and finally plots the
histogram on PC terminal along with the above information.
3. RESULTS AND CONCLUSION
Image
processing represents a set of techniques with vital application in diverse
areas like manufacturing, industrial quality control, medical imaging, remote
sensing, etc.
Image processing involves sampling, digitization, storage and processing of analog picture information.
All the programs developed are integrated under one menu. The software works for any image of size 512X512 maximum.
4. ANNEXURE
DIGITAL IMAGE PROCESSING
DIGITAL IMAGE PROCESSING
The
word Digital relates to “Calculation by numerical methods or by discrete
units.”
We
can now define a digital image to be a numerical representation of an
object.
Processing
is the act of subjecting something to a process. A process is a series of actions or
operations leading to a desired result.
Thus a series of actions or operations are performed upon an object to
alter its form in a desired manner.
The
restricted definition of a digital image is a sampled, quantized
function of two dimensions which has been generated by optical means, sampled
in an equally spaced rectangular grid pattern, and quantized in equal intervals
of gray level.
Digital
image processing involves the manipulation and interpretation of digital images
with the aid of computer. This form of
image processing brought by the advent of the digital computer. The data has to be converted into digital
form using various scanners. Allowing
the precise implementation of the processes, this form provides the greatest
flexibility and power for general image processing applications.
·
CONTAST refers to the amplitude of gray level
variations within an image.
·
NOISE is broadly defined as an additive (or
multiplicative) contamination of an image.
· THE
SAMPLING DENSITY of a digital
image is the number of sample points per unit measure in the domain.
· GRAY
SCALE RESOLUTION is the number
of gray levels per unit measure of image amplitude.
·
MAGNIFICATION refers to the size relationship between an
image and the object or image it represents.
Magnification is a meaningful
relationship between input and output digital images in a processing step.
ELEMENTS OF DIGITAL IMAGE PROCESSING :
Digital
Image Processing basically requires a computer upon which to process
images. In addition, the system must
have two pieces of special input/output equipment, an image digitizer and an
image display device.
But,
computers work with numerical rather than pictorial data, an image must be
converted to numerical form before processing.
This conversion process is called Digitization. After processing, the final product is
displayed by a process that is the reverse of digitization.
The
image is divided into small regions called picture elements or pixels or
pels. Each pixel has a location or address
(line or row number and sample or column number) and an integer value called
the gray level. Gray level represents
the brightness or darkness of the point.
ADVANTAGES OF DIGITAL IMAGE PROCESS :
In
industries, products have to be checked for quality before dispatching. These are tested using Non-destructive
Evaluation methods like X-ray, Ultrasonic imaging and Acoustic Emission
techniques. Because of limitations of the
outputs from above techniques, minor details may not be seen clearly. So, these can be converted into digital form
using various scanners and images can be enhanced using digital image
processing techniques to know the defects clearly.
The
principle advantage of digital image processing methods are their versatility,
repeatability and the preservation of original data precision.
FACTORS TO INDICATE A LIVELY FUTURE FOR DIGITAL IMAGE
PROCESSING :
1) The declining cost of computer equipment.
2) Increasing availability of the equipment.
3) Several new technological trends, which include
parallel processing made practical by low-cost microprocessors, and the use of
Charge Coupled Devices (CCD).
4) Medical diagnosis.
5) Remote sensing programs.
(I) DIGITAL IMAGE REPRESENTATION
The
term monochrome image refers to a two dimensional light intensity function f(x,
y). Where x and y denotes spatial
co-ordinates and the value of f at any point (x, y) is proportional to the
brightness (or gray-level) of the image at that point. A digital image is an image f(x, y) that has
been discretized both in spatial coordinates and in brightness. We may consider a digital image as a matrix
whose row and column indicate a point in the image and the corresponding matrix
element value identifies the gray level of that point. The elements of such a digital array are
called image elements or picture elements or pixels or pels with last two names
being used as abbreviations of picture elements.
(II) IMAGE MODEL
The
term image refers to a two dimensional light intensity function denoted by f(x,
y), where the value or amplitude of f at spatial coordinates (x, y), gives the
intensity of the image at that point.
Since
light is a form of energy, f(x, y) must be non zero and finite, i.e., 0 <
f(x, y) < x, the image we receive in our everyday visual activities normally
consists of light reflected from objects.
The basic nature of f(x, y)
may be considered as being characterized by two components. One component is amount of source incident on
the scene being viewed while the other is amount of light reflected by the
objects in the scene. These components
are appropriately called the illumination and the reflectance components and
are denoted by i(x, y) and r(x, y) respectively.
The
functions i(x, y) and r(x, y) combine as a product to form f(x,
y).
0 < f(x, y) < x ---------------à eq(2.1)
f(x, y) = i(x, y) * r(x, y) ---------------à eq(2.2)
where 0
< i(x, y) < x --------------à eq(2.3)
0 <= r(x, y)
<= 1 --------------à eq(2.4)
Eq(2.1)
indicates the fact that reflectance is bounded by 0 (total absorption)
and 1 (total reflectance). The
nature of i(x, y) is determined by the characteristics of the objects in
the scene.
The
intensity of a monochrome f at coordinates (x, y) will be called
the gray level of the image at that point.
From the above equations it is evident that l lies in the range.
lmin <= l <= lmax --------------à eq(2.5)
In
theory, the only requirement one lmin is that it be positive and lmax is that
it be finite in practice.
lmin = i(min) * r(min) and lmax =
i(max) * r(max)
The
interval lmin to lmax is called the gray scale. It is a common practice to shift this
interval numerically to the interval (0,L), where l = 0 is considered
black and l = L is considered white in the scale. All intermediate values are shades of gray
varying continuously from black to white.
(III) FUNDAMENTAL STEPS IN
DIGITAL IMAGE PROCESSING
The first step in the process is image
acquisition – that is, to acquire a digital image. To do so requires an imaging sensor and the
capability to digitize the signal produced by sensor. After the digital image has been obtained,
the next step deals with preprocessing that image. The key function of preprocessing is to
improve the image in ways that increases the chances for success of the other
processes. For example, enhancing
contrast, removing noise, and isolating regions, etc.
The next stage deals with segmentation. Segmentation partitions an input image into
its constituent parts or objects. The
outputs of the segmentation stage usually a raw pixel data. Choosing a representation is only part of the
solution for transforming raw data into suitable form for subsequent computer
processing. Description is also called feature
selection, delay with extracting features that result in some quantitative
information of interest or features that are basic for differentiating one
class of objects from another.
The last stage involves recognition and
interpretation. Recognition is the process that assigns a label to an
object based on the information provided by descriptors. Interpretation involves assigning meaning to
an ensemble of recognized objects.
Knowledge about a problem domain is coded into
an image processing system in the form of a knowledge base. Thus it limits the search that has to be
conducted in seeking that information.
(IV) IDEA ABOUT IMAGE ENHANCEMENT TECHNIQUES
Most
of the raw materials are to be purified, similarly most of raw images are to be
enhanced if need be. In fact, most of
the pictures obtained either from space probe or from microscope are not very
clear and most of them can be made better.
This pictures need to be enhanced in one-way or the other to make them
better and more perceptible.
The popular enhancement techniques often
applied are :
A)
Contrast
Stretching or Enhancement
B)
Image
Filtering
C)
Image
ratioing or Image Transformation
A) CONTRAST STRETCHING OR ENHANCEMENT :
Some times the images (e.g., over water
bodies, deserts, dense forests, snow, clouds and under heavy conditions over
heterogeneous regions) are homogeneous, i.e., they do not have much change in
their levels. In terms of histogram
representation, they are characterized as the occurrence of very narrow
peaks. The homogeneity can also be due
to the incorrect illumination of the scene.
Ultimately the pictures hence obtained are not
easily interpretable or sometimes poor human perceptible. This is because there exists only a narrow
range of gray levels in the image having provision for wider range of gray
levels. The contrast stretching methods
are designed exclusively frequently encountered situations. Different stretching techniques have been
developed to stretch the narrow range to the whole of the available dynamic
range. These techniques can broadly be
classified as Liner and Non-linear stretching.
B)
IMAGE FILTERING :
Spatial frequency is the number of
changes in the brightness values per unit area in any part of an image. If the spatial frequency is low, it is called
low frequency area and if it is high, it is called high frequency area. Algorithms performing the enhancement of
images by suppressing are de-emphasizing certain frequency and passing are
emphasizing certain other frequencies are called filters. Filters that pass high frequency is, and
hence emphasize fine details and edges are referred as high frequency filters
conversely, low frequency filters suppress the high frequency content of the
imagery while emphasizing gradual change.
The
resultant of this filtering of the image is by changing the brightness value of
the original image in a particular fashion.
The brightness value of the pixel at (u, v) coordinates of an image, BV
is changes to BV’ by applying the mask A. The coefficients/kernal values are multiplied
as shown below:
C(1, 1)*BV(u-1, v-1) C(1, 2)*BV(u-1, v) C(1,
3)*BV(u-1,
v+1)
C(2, 1)*BV(u, v-1) C(2, 2)*BV(u, v) C(2,
3)*BV(u,
v+1)
C(3, 1)*BV(u+1, v-1) C(3, 2)*BV(u+1, v) C(3,
3)*BV(u+1,
v+1)
The new values from the filtering operation BV’
is calculated as :
BV’(u, v) = 1/N Eij { C(i, j) * BV(u-2+i, v-2+j) }
C)
IMAGE RATIOING OR IMAGE
TRANSORMATION :
The theme of the technique of
magnification is to the closer view by magnifying or zooming the interested
part in the imagery by reduction, we can bring the unmanageable huge amount of
data to a mount limit in order to display the imagery.
i)
MAGNIFICATION
:
This is usually done to improve the scale of
display for visual interpretation or sometimes to match the scale of one image
to another. To magnify an image by a
factor of m2. Each pixel of the original image is replaced by
a block of mXm pixels, all with the same brightness value as the original
pixel.
ii)
REDUCTION
:
To reduce a digital image to 1/m2 of the original data, every mth column of the original imagery is selected and
displayed. Another way of accomplishing
the same is by taking the average in mXm block and displaying this average
after proper rounding.
(V) THE GRAY LEVEL HISTOGRAM
One
of the simplest and most useful tools in digital image processing is the gray
level histogram. This function
summarizes the gray level content of an image.
While the histogram of any image contains considerable information,
certain types of images are completely specified by their histograms. Computation of the histogram is simple and
may be done at little apparent cost during transfer of any image from one data
set to another.
The
gray level histogram is a function showing, for each gray level, the number of
pixels in the image that have the same gray value level.
This
has become the most frequent form of representation of an image because it
contains many properties to the image.
By looking at the histogram, one can find out the nature of the image
and by using it, one can explain his techniques or implementations as well as
the results.
(VI) USES OF HISTOGRAM
A) DIGITIZING PARAMETERS :
The histogram indicates whether an image is
properly scaled within the available range of gray levels. A digital image should make use of almost all
the available gray levels.
B)
BOUNDAY THRESHOLD SELECTION :
The gray level corresponding to minimum between
the two peaks in histogram is optimal for defining the boundary. Given the histogram, we could determine an
optimal threshold gray level for the object and compute its area without even
seeing the image.
BIBLIOGRAPHY
1.
DIGITAL
IMAGE PROCESSING
Addison Wesley Publication Company
– GONZALEZ & WINTZ
2.
DIGITAL
IMAGE PROCESSING - JENSON
3.
DIGITAL
IMAGE PROCESSING – KENNETH R. CASTLEMAN
4.
PATTERN
RECOGNITION LETTERS Volume 13, 1992
5.
IMAGE
ENHANCEMENT AND RESTORATION – R.C. GONZALEZ
6.
DIGITAL
PICTURE PROCESSING – AZRIEL ROSENFELD AND
AVINASH
C. KAK
7.
PRAGMATIC
DIGITAL IMAGE PROCESSING – BILLINGSLEY F.C.
8.
COMPUTER
VISION, GRAPHICS & IMAGE PROCESSING
Volume 29, 1985
9.
COMPUTER
VISION, GRAPHICS & IMAGE PROCESSING
Volume 41, 1988
10.
COMPUTER
VISION, GRAPHICS & IMAGE PROCESSING
Volume 56, 1988
THE END
