Intensity Enhancement in Gray Level Images using HSV Color Coding Technique

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1557
INTENSITY ENHANCEMENT IN GRAY LEVEL IMAGES USING HSV COLOR
CODING TECHNIQUE
Er.Harteesh Kumar1, Dr.Rahul Malhotra2, Er.Ashish Grover3
1M.Tech (Student), Department Of E.C.E GTBKIET Chhapianwali, Malout, PUNJAB, INDIA
2DIRECTOR & PRINCIPAL, GTBKIET Chhapianwali, Malout, PUNJAB, INDIA
3Assistant Professor, Department Of E.C.E GTBKIET Chhapianwali, Malout, PUNJAB, INDIA
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Abstract The capability will increase the efficiency by
decreasing the time required to visualize the image clearly
and reduce the probability of errors due tofatigue.Thework
in this thesis is motivated from the practical point ofview by
several shortcomings of traditional methods. The first
problem is the inability of all known traditional methods to
properly segment the objects from the background without
the interference from object shadows and highlights.
Moreover there is inadequate research on the combination
of hue- and intensity-based similarity measures to improve
color. The ineffective use of color can degrade anapplication
reference and lesson user application. TheHSVcolorspaceis
quite similar to the way in which humansperceivecolor.The
other models define color in relation to the primary colors.
RGB is not efficient since it uses equal bandwidth for each
color component. However, human eye is more sensitive to
the luminance component than the color component. Thus,
many image coding standards use HSV color scheme.
Key Words: HSV, Pixels, RGB, CIE, Gray Scale Image
1.INTRODUCTION
This introduction is a practical guide to the challenges, and
the hardware and algorithms used to meet them. Image
processing modifies pictures to improve them
(enhancement, restoration), extract the information
(analysis, recognition), and change their structure
(composition, image editing). Images can be processed by
optical, photographic, and electronic means, but image
processing using digital computers is the most common
method because digital methods are fast, flexible, and
precise. An image can be synthesized from a micrograph of
various cell organelles by assigning alight intensity value to
each cell organelle. The sensor signal is “digitized”--
converted to an array of numerical values, each value
representing the light intensity of a small area ofthecell.The
digitized values are called picture elements, or “pixels,” and
are stored in computer memory as a digital image. A typical
size for a digital image is an array of 512 by 512 pixels,
where each pixel has value in the range of 0 to 255. The
digital image is processed by a computer to achieve the
desired result. Image enhancement improves the quality
(clarity) of images for human viewing. Removing blurring
and noise, increasing contrast, and revealing details are
examples of enhancement operations. Reducing the noise
and blurring and increasing the contrast range could
enhance the image. The original image might have areas of
very high and very low intensity, which mask details. An
adaptive enhancement algorithm reveals these details.
Adaptive algorithms adjust their operation based on the
image information (pixels) being processed. In this case the
mean intensity, contrast, and sharpness (amount of blur
removal) could be adjusted based on the pixel intensity
statistics in various areas of the image. Images are produced
by a variety of physical devices, including still and video
cameras, x-ray devices, electron microscopes, radar, and
ultrasound, and used for a variety of purposes, including
entertainment, medical, business (e.g. documents),
industrial, military, civil (e.g. traffic),security, and scientific.
The goal in each case is for an observer, human or machine,
to extract useful information about the scene being imaged.
Often the raw image is not directly suitable for this purpose,
and must be processed in some way. Such processing is
called image enhancement; processing by an observer to
extract information is called image analysis. Enhancement
and analysis are distinguished by their output, images vs.
scene information, and by the challenges faced and methods
employed. Image enhancement has been done by chemical,
optical, and the electronic means, while analysis has been
done mostly by humans and electronically. Digital image
processing is a subset of the electronic domain wherein the
image is converted to an array of small integers,called pixels,
representing a physical quantity such as scene radiance,
stored in a digital memory, and processed by computer or
other Digital hardware. Digital image processing, either as
enhancement for human observers or performing
autonomous analysis, offers advantages in cost, speed, and
flexibility, and with the rapidly falling price and rising
performance of personal computers it has become the
dominant method in use
1.1Concept Of Image
Perhaps the first guiding principal is that humans are better
at judgment and machines are better atmeasurement.Along
these lines image enhancement, which generally requires
lots of numeric computation but little judgments, is well-
suited for digital processing. On top of this many digital
image processingapplicationsareconstrainedbysevere cost
targets. Thus we often face the engineer’s dreaded triple

curse, the need to design something good, fast, and cheap all
at once.
1.1.1 Types of Digital Images
There are two important types of digital images—color and
black and white. Color images are made up of colored pixels
while black and white images are made of pixels in different
shades of gray.
(a) Black and White Images
A black and white image is made up of pixels each of which
holds a single number corresponding to the gray level of the
image at a particular location. These gray levels span the full
range from black to white in a series of very fine steps,
normally 256 different grays. Since the eye can barely
distinguish about 200 different gray levels, this is enough to
give the illusion of a steeples tonal scale .Assuming 256 gray
levels, each black and white pixel can be stored in a single
byte (8 bits) of memory.
(b) Color Images
A color image is made up of pixels each of which holds three
numbers corresponding to the red, green, and blue levels of
the image at a particular location. Red, green, and blue
(sometimes referred to as RGB) are the primary colors for
mixing light-these so-called additive primary colors are
different from thesubtractiveprimarycolorsusedformixing
paints (cyan, magenta, and yellow). Any color canbecreated
by mixing the correct amounts of red, green, and blue light.
Assuming 256 levels for each primary, each color pixel can
be stored in three bytes (24 bits) of memory. This
corresponds to roughly16.7 milliondifferentpossiblecolors.
Note that for images of the same size, a black and white
version will use three times less memory than a color
version.
(c) Binary or Bi-level Images
Binary images use only a single bit to represent each pixel.
Since a bit can only exist in two states—on or off, every pixel
in a binary image must be one of two colors, usually black or
white. This inabilitytorepresentintermediateshadesof gray
is what limits their usefulness in dealing with photographic
images.
(d) Indexed Color Images
Some color images are created using a limited palette of
colors, typically 256 different colors. These images are
referred to as indexed colorimagesbecausethedata foreach
pixel consists of a palette indexindicating whichofthecolors
in the palette applies to that pixel. There are several
problems with using indexed color to represent
photographic images. First, if the image contains more
different colors than are in the palette, techniques such as
dithering must be applied to represent the missing colors
and this degrades the image. Second,combiningtwo indexed
color images that use different palettes or even retouching
part of a single indexed color image creates problems
because of the limited number of available colors.
2. HARDWARE
Lights: All image processing applications start with some
form of illumination, typically light but more generallysome
form of energy. In some cases ambient light must be used,
but more typically the illumination can be designed for the
application. In such cases the battle is often won or lostright
here—no amount of cleversoftwarecanrecoverinformation
that simply isn’t there due to poor illumination. Generally
one can choose illumination intensity, direction, spectrum
(color), and continuous or strobe. Intensity is easiest to
choose and least important; any decent image processing
algorithm should be immune to significant variations in
contrast, although applications that Demand photometric
accuracy will require control and calibration of intensity.
Direction is harder to choose and more important, as any
professional photographer knows.
3. LINEAR FILTERS
Linear filters amplify or attenuate selected spatial
frequencies, can achieve such effects as smoothing and
sharpening. and usually form the basis of re-sampling and
boundary detection algorithms. Linear filters can be defined
by a convolution operation,whereoutputpixelsareobtained
by multiplying each neighborhood pixel by a corresponding
element of a like shaped set of values called a kernel, and
then summing those products.. Figure1. a, for example,
shows a rather noisy image of a cross within a circle.
Convolution with the smoothing (low pass) kernel of figure
1.3.6.2b produces figure 1.3.6.c. In this example the
neighborhood is 25 pixels arranged in a 5x5 square. Note
how high frequency noise has been attenuated, but at a cost
of some loss of edge sharpness. Note also that the kernel
elements sum to 1.0 for unity gain. The smoothing kernel of
figure 2b is a 2D Gaussian approximation.The2DGaussianis
among the most important functionsusedforlinearfiltering.
Its frequency response is also a Gaussian, which results in a
well-defined pass band and no ringing. Kernels that
approximate the difference of two Gaussiansofdifferentsize
make excellent band-pass and high-pass filters. Figure
3.1dillustrates the effect of a band-pass filter based on a
difference of Gaussian approximation using a 10x10 kernel.
Both the high frequency noise and the low frequency
uniform regions have been attenuated, leavingonlythemid-
frequency components of the edges.

Figure-1: An image enhanced to reduce noise or
emphasize boundaries.
Physiological Factors Color is determined by an interaction
among three photo pigments; the perceived color is a
mixture of the relative responses of the red, green, and blue
photo pigments, in much the same way as a television
camera creates color [4]. Given a dramatic imbalanceamong
the percentages ofcellscontainingred(approximately 64%),
green (approximately 32%), and blue (approximately 2%)
photo pigments, it is clear that the perception ofcolorisboth
highly specialized and physiologically biased (data from
March, 1988).
Color results from the interaction of light with the nervous
system. There are several components that affect color
perception, including the eye lens, the retina, and a color
processing unit along the optic nerve. These areas are
discussed in the following sections.
(a) Lens
The function of the lens is to focus the incoming light on the
retina, which contains the photoreceptors. Different
wavelengths of light have different focal lengths. Therefore,
for pure hues, the lens must change its shapesothatthelight
is focused correctly. For a given lens curvature, longer
wavelengths have a longer focal length, i.e., redisthelongest
focal length and blue is the shortest. To have an image
focused on the retina, the lens curvature must change with
wavelength with red light requiring the greatest curvature
and blue light the least curvature. This means that if pure
blue and pure red hues are intermixed, the lens is constantly
changing shape and the eye becomes tired.
A related effect is called chromo here pure colors located at
the same distance from the eye appear to be at different
distances, e.g. reds appear closer and blues more distant.
Sometimes pure blues focus in front of the retina and so
appear unfocused. At night, a deep blue sign may appear
fuzzy while other colors appear sharp. The lens also absorbs
light about twice as much in the blue region as in the red
region. As people age the lens yellows, which means it
absorbs more in the shorter wavelengths. Therefore, the
result is that people are more sensitive to longer nets,
wavelengths (yellows and oranges) than they are to shorter
wavelengths (cyan to blue)andtheseincreaseswithage. The
fluid between the lens and the retina also absorb light and
this effect increases as people age,sotheolder peopleget the
less sensitive they are to light in general (the apparent
brightness level decreases) and especially the sensitivity to
blue decreases.
(b) Retina
The retina contains the photoreceptors that absorb photons
and transmit chemical signals to the brain. There are two
types: rods, which are night-vision receptors and have no
color dependency, and cones, which have color sensitivity
and require a higher level of light intensity than rods.
Figure-2: Spectral sensitivities of the three classes of
photoreceptors in the retina
As shown in Figure2. there are three types of photo-
pigments in the cones; "blue" with a maximum sensitivity at
430 nm, "green" with a maximum sensitivity at 530 nm, and
"red" at 560 nm. (This wavelength actually corresponds to
yellow). Light at a single wavelengthwill partiallyactivateall
three types of cones, e.g. at a wavelength of 470 nm, blue is
strongest plus some red and green components. The
percentage of cones is not equal but is as follows: blue (4%),
green (32%), and red (64%). In addition, the cones are
differentially distributed in the retina. The center of the
retina has a dense concentration of cones but no rods while
the periphery has many rods but few cones. The color
distribution is also asymmetrical. The center of the retinas
primarily green cones,surroundedbyred-yellowcones, with
the blue cones being mainly on the periphery. The center of

the retina has no blue cones. Objects are seen by edge
detection, where an edge can be created by a difference in
color or brightness or both. Edges formed by color
differences alone, with no brightness differences, appear
fuzzy and unfocused, so changes in brightness should be
added to get sharp edges.
Photoreceptors adjust their sensitivity to the overall light
level, e.g. going into or out of a dark room require some
adjustment time. There is also a requiredminimumintensity
level for the photoreceptors to respond. This minimum
varies with wavelength with the highest sensitivity in the
center of the spectrum. Therefore, blues and reds must have
a higher intensity than greens or yellows in order to be
perceived.
(c) Brain
From the retina, the optic nerve (actually a collection of
nerves) connects to the brain but before it reaches thebrain,
there is a color-processing unit, called the lateral
geniculation body. This recombines the RGB color
information into three new channels as
Follows:
R-G gives red or green color perception
R+G gives the perception of brightness and yields yellow(Y)
Y-B gives yellow or blue color perception
Thus, blue plays no part in brightness so thatcolorsdiffering
only in amount of blue don't produce sharp edges. Also,note
that since blue and yellow and red & green are Linked
together it is impossible to experience combinationssuchas
reddish green or bluish yellow.
(d) Color blindness
About nine percent of the population has some kind of color
perception problem. The most common is red-green
deficiency, which can arise froma deficiencyof eitherthered
or the green photo-pigments. These people have difficulty
distinguishing any color that is dependent upon the red:
green ratio.
4. GENERAL GUIDELINES BASED ON PHYSIOLOGY
These are some of the guidelines drawn from Munch [5]
principles based on physiology. Avoid the simultaneous
display of highly saturated, spectrally extreme colors. This
causes the lens to rapidly change shape and thus tires the
eyes. Desiderate the colors or else use colors that are close
together in the spectrum.
Pure blue should be avoided for text, thin lines, and small
shapes. Since there are no blue cones in the center of the
retina, these are difficult to see. But blue makes an excellent
background color, e.g. for a computer display it tends to blur
the raster lines.
Avoid adjacent colors that differ only in the
amount of blue. Since blue doesnot Contributetobrightness,
this creates fuzzy edges.
Older operators need higher brightness levels to distinguish
colors.
Colors change in appearance as the ambient light level
changes.
The magnitude of a detectable change in color varies across
the spectrum.
It is difficult to focus upon edges created by color alone.
Avoid red and green in the periphery of large displays.
Opponent colors go well together.
For color-deficient observers, avoidsinglecolordistinctions.
Color selection guidelines based on human color vision
Avoid adjacent areas of strong blue and strong red in a
display to prevent unwanted depth effects(colorsappearing
to lie in different planes).
Never use the blue channel alone for fine detail such as text
or graphics. Do not use, for example, blue text on a black
background or yellow text on a white background.
Areas of strong color and high contrast can produce after
images when the viewer looks away from the screen,
resulting in visual stress from prolonged Viewing.
Do not use hue alone to encode information in applications
where serious consequences might ensue if a color-deficient
user were to make an incorrect Selection.
5. PSYCHOLOGICAL FACTORS
As well understood as the physiology of color is, this factor
provides little explanationforouropinionsofcolorandcolor
combinations. At the very least, opinionsofcolorarelearned
and highly associative. For example, as children, we often
had a “favorite color” and we liked everything: clothes, toys,
books that matched our preference. Over time, we learned a
variety of color schemes and in most cases, our tastes
become more refined. But even as adults, we are influenced
by fashion, and may still associate our more sophisticated
sense of color with increasingly more sophisticated
emotions, desires, or impressions. For example, even a
cursory examination of changes in interior design from the
1950s to the present reveals a dramatic evolution of what
was considered warm or even comfortable colour.

6. ALGORITHM BACKGROUND
6.1 Color Space
Fig-3: 3 D representation of the human color space.
One can picture this space as a region in three-dimensional
Euclidean space if one identifies the x, y, and z axes with the
stimuli for the long-wavelength (L), medium-wavelength
(M), and short-wavelength (S) receptors. The origin, (S,M,L)
= (0,0,0), corresponds to black. White has no definite
position in this diagram; rather it is defined according to the
color temperature or whitebalanceasdesiredorasavailable
from ambient lighting. The human color space is a horse-
shoe-shaped cone such as shown here extending from the
origin to, in principle, infinity.
6.2 The CIE Color Model
Though some colors can be created by a single, pure
wavelength, most colors are the result of a mixture of
wavelengths. A French organization, the Commission
International de L'Eclairage (CIE), worked in the first half of
the 20th century developing a method for systematically
measuring color in relation to the wavelengths they contain.
This system became known as the CIE color model (or
system). The model was originally developed based on the
tristimulus theory of color perception. The theory is based
on the fact that our eyes contain three different types of
color receptors called cones. These three receptorsrespond
differently to different wavelengths of visible light. This
differential response of the three cones is measured in three
variables X, Y, and Z in the CIE color model.
Notice in Figure 3b that the perimeter edge marks the
wavelengths of visible light. Along thisedgewill bethe'pure'
spectral light colors. Other colors are developed by mixing
varying amountsofdifferent wavelengths.Noticethepurples
at the bottom do not have a wavelength associated with
them. non-spectral colors
Fig-4: CIE color model.
7. RESULT AND DISUSSION
7.1 Implementation of Color Coding Methods
The output response of the threecolorcodingtechniques are
plotted (using MATLAB) and are represented as Rainbow
Transform, Phase and Frequency Transform and HSV
Transform. Also the results are compared for the different
input values and outputs of three diffe methods.4a Example
1- Gray Scale Image ColorCoding Frequency Transform are
also plotted for different Input of this image for Rainbow
method Phase and Frequency Transform and HSV method
are plotted along with their Big view
A color model is an abstract mathematical model describing
the way colors can be represented as tulles of numbers,
typically as three or four values or color components. When
this model is associated with a precise description of how
the components are to be interpreted (viewing conditions,
etc.), the resulting set of The most saturated colors are
located at the outer rim of the region, with brighter colors
farther removed from the origin. The human tristimulus
space has the property that additive mixing of colors
corresponds to the adding of vectors in this space. This
makes it easy to, for example, describe the possible colors
(gamut) that can be constructed from the red, green, and
blue primaries in a computer display.colors is called color
space. This section describes ways in which human color
vision can be modeled.
6.1 Tristimulus color space

Figure- 4(a): Input Gray Scale Image of Cell Figure -4(c): The Output Response of HSV Method for
Gray Scale Cell image.
The Image color coding methods are implemented on gray
scale image of Bag containing threat element having
specifications of phase (p) = 1.78 and frequency (f) = 8.29 .
The output response of this image for Rainbow method
Phase and Frequency Transform andHSVmethodareploted
alongwith their Big view. The different output response of
Phase and Frequency Transform are also plotted for
different Input Phase and Frequency parameters.
8. CONCLUSION
Among the different coloring schemes the HSV scheme that
was developed based on the color survey result was ranked
highest . However, the other color maps were ranked very
close to the HSV map. The cosine color map results were
impressive. The difficulty with the HSV map is to set or pick
the threshold. This can be solved by establishing an auto
thresholding algorithm. The cosinecolormapproducedvery
continuous and smooth results when compared to the other
maps. In addition, color-coding already enhanced images
may produce betterresults.Currentlycolor-codingisapplied
directly to the intensity stretched image. This work shows
that 90% of the edges are about the same in graylevel and in
color images whereas using color coding technique10% left
over edges may be extracted from the color images.
To acquire statistically significant results, the imagesshould
be presented in a pseudo random fashion to avoid the
influence of other images in detecting the threat. False
positives should be evaluatedbyintroducingimageswithout
any threat
Figure -4(b): The Output Response of Ranibow
Transform and Phase and Frequency Transform

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