Image Enhancement Techniques Final (1)

8/18/2019 Image Enhancement Techniques Final (1)

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S-Voup-B

Image

Enhancement

Research Paper

bmitted by: Shahbano (0!"#mba$ %a& (0'0"Submitted to: )s* )ehmoonaanum

eory o .utomata


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Study of Image Enhancement Techniques for Grayscale Images

Sumbal Naz1, Shahbano2, Memoona hanam!

12!"om#uter Science $e#artment, %atima &innah 'omen (ni)ersity

*bstract

The presented paper discuss different techniques of

image enhancement like histogram equalization

which is explained in detail with examples and

sample data of 8x8 matrix is also processed, point

processing techniques which includes linear,

logarithmic and power law transformations. Along

with the description of these techniques code is also

implemented in MATLAB with the help of general

forms and the results of the processed images are

shown in the form of enhanced images. These

techniques are used to enhance different tpes of

images like some are used to enhance satellite

images, some are used for x!ra images and some are

used for dark images. "ur main emphasisis on gra

scale images.

ey+ords #mage enhancement, $istogram

equalization %$&', spatial domain, (ra Le)el

Transformation, Linear Transformation, Logarithmic

transformation, *ower law.

1- Introduction

#mage processing has a wide range of applications in

computer )ision, multimedia communication,

tele)ision +roadcasting, etc. that demand )er goodqualit of images. The qualit of an image degrades

due to introduction of noise during acquisition,

transmission reception and storage retrie)al

processes. An image with high contrast and

+rightness is called fine qualit image while a poor

qualit image is defined + low contrast and poorl

defined +oundaries +etween the edges. -or +etter

understanda+ilit of image we use #mage

enhancement technique refers to sharpening of image

features such as edges, +oundaries or contrast to

make it more useful. The fundamental goal of image

enhancement is to process the input image in such awa that the output image is more suita+le for

interpretation + the humans as well as + machines.

suall two tpes of images are enhanced, haz

images and t color images. #mage enhancement is the

most interesting and the )isuall appealing areas of

image processing. Techniques used for grascale

image enhancement are histogram stretch, histogram

equalization and adapti)e contrast enhancement.

$owe)er, our main emphasis is on the histogram

equalization. /01

"ne technique cannot +e used for all tpe of images,

for this reason image enhancement approaches a

ma2or categor which is spatial domain. #t refers to

the direct manipulation of pixel in an image. #n

spatial domain there are three +asic gra le)el

transformations, which are linear, logarithmic and

*ower!law /01.

Techniques are implemented using Matrix

La+orator %MATLAB' which pro)ide a+ilit to

write the algorithm in a high!le)el programming

language with +uilt in )isualization tools.

2- .iterature re)ie+

#n this section we stud techniques which are alread

reported like histogram equalization and spatial

domain. $istogram equalization is an image

enhancement technique. #t is simple and popular

technique +ut still it has some limitations. But due to

these limitations se)eral histogram equalization

methods has +een de)eloped. $istogram equalization

causes the mean luminance of the image, produces

artifacts and unnatural enhancements and does not

consider local information in its process. 3e alsodefine the other processing techniques of #mage

enhancement. Thus the contri+ution of this paper is to

classif and re)iew image enhancement processing

techniques. /41

5ontrast enhancement is considered as an

optimization pro+lem that minimizes the cost.

$istogram equalization is an efficient technique for

contrast enhancement. $owe)er, con)entional

histogram equalization %$&' tpicall results in

excessi)e contrast enhancement, which in turn gi)es

the processed image. But + introducing special

terms of the histogram equalization the le)el of

contrast enhancement can +e ad2usted, noise

ro+ustness6 stretching and +rightness can also +e

impro)ed to a high le)el. /71

#n writing, man efficient digital image filters are

found that perform well under low noise conditions.

But their performance is not so good under fair andhigh noise conditions. Thus, it is felt to de)elop well!


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organized +ut simple algorithms to restrain modest

and high power noise in an image. oise in digital

images is found to +e additi)e in nature. 9uch a noise

is referred to as Additi)e 3hite (aussian oise%A3('. #mage de!noising is usuall essential to +e

performed in order to do segmentation, feature

extraction, o+2ect recognition, texture analsis, etc.

The purpose of de!noising is to suppress the noisequite effecti)el while smoothing the edges and the

other detailed features as much as possi+le. /:1

$istogram &qualization %$&' is a simple and

effecti)e image enhancement technique. But, it tends

to change the mean +rightness of the image, +ut it

does not work well for consumer product. To

preser)e +rightness and to enhance contrast of

images, there are man methods which are to +e

introduced, +ut man of them present unwanted

artifacts such as intensit saturation, to increase the

enhancement and to amplif the noise. A)aila+lehistogram equalization methods are re)iewed and

compared with other methods of image processing in

order to contrast the +rightness and other areas of the

image, and also to e)aluate contrast enhancement. /;1

#mage &nhancement is one of the most essential andcomplicated techniques in image research. The aim of

image enhancement is to reco)er the )isual form of

an image, or to pro)ide a pleasing effect in an image.

Man images like medical images, satellite images,aerial images and e)en real life photographs suffer

from poor contrast and noise. #n this wa it is

required to enhance the image and to impro)e thequalit so that it gi)es the pleasing effect. #mage

&nhancement techniques in medical images detection

and analsis, remo)e +lurring and noise, increasing

contrast, and re)ealing details. The existingtechniques of image enhancement can +e classified

into two categories< 9patial =omain and -requenc

domain enhancement. #n this paper, we present an

o)er)iew of image enhancement processingtechniques in spatial domain. />1

The main purpose of image enhancement is to

practice an image so that result is more pleasing thanthe original image. =igital image processing

techniques has two domains< spatial domain and

frequenc domain. #mage enhancement plas a )ital

role in e)er field where images ha)e to +eunderstood and analzed. Man images like medical

images, satellite images, microscop images, aerial

images and e)en real life photographs go through

from poor contrast and noise. #t is necessar toenhance the contrast and remo)e the noise to increase

image )isual qualit. This paper focuses on different

image enhancement techniques and +etter approach

for future research. /?1

!- /istogram Equalization

$istogram &qualization is a technique where the

histogram of the resultant image is as flat as possi+le.

The theoretical +asis for histogram equalization

in)ol)es pro+a+ilit theor, where the histogram is

treated as the pro+a+ilit distri+ution of the gra

le)els. /01

Assuming @ to +e an image whose pixel )alue is

accessed as @ %i,2'. #t is composed of L discrete gra

le)els denoted + @,@0,C,@L!0D. $ere, @%i,2'

represents the intensit of the image at spatial

location %i,2' with the condition that @%i,2'& @,@0,

C,@L!0D. As the intensities are all discrete )alues,the histogram of a digital image is a discrete function

/41.

$istogram h is defined as h%@k )=nk ,

for k E, 0, C, L!0

3here @k is the k!th gra le)el and n k presents the

num+er of times that the gra le)el @k appears in the

image. #n other words, the histogram is the frequenc

of occurrence of the gra le)els in the image /41.

$and calculation for +etter understanda+ilit are

performed. Ta+le 0 contain randoml generated data

for 8x8 arra to represent and image. -igure 0 shows

"riginal image of pixel )alues in Ta+le 0.

Table 1 0riginal data of array of !3bit image

4 7 ; 7 4 7

4 4 0 7 0 4

7 7 7 : ; 0

4 7 0 4 7 7 : >

? ? > 4 > : > ;

> : ? 0 ? 0 ; :

> > ; 4 4 0 0

> ? ; ; 4 0


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%igure 1 !3bit image of array

Ta+le 4 shows calculations. 9tep F 0 include num+er

of each pixel. #n 9tep F 4 Gunning 9um is calculated.

9tep F 7 shows normalization and then multiplication

+ highest intensit )alue i.e. ?. #f the image is 8 +it,

multipl + 4;;. Ta+le 7 consists of 9tep F : which

shows new image. 9tep F > num+er of each gra le)el

of new image. -igure 4 shows new 7!+it image

o+tained after calculations.

Table 2 4esults of "alculations for array

Ste# 5 1

(ra le)el 0 4 7 : ; > ?

o. of

pixels

H H 0

0

0

; ? 8 ;

Ste# 5 2

Gunning

9um

H 0

8

4

H

7

H

:

:

;

0

;

H

>

:

Ste# 5 !

ormalizi

ng andmultiplin

g + ?

0 4 7 : ; > > ?

Table ! Ne+ !3bit image

Ste# 5 6

0 0 7 : > : 7 :

0 7 7 4 : 4 0 7

: : 0 : ; > 0 4

7 : 4 7 : : ; >

? ? > 7 > ; > >

> ; ? 4 ? 4 > ;

> > > 7 7 4 4 0

> ? > > 7 4 0 0Ste# 5 7

(ra

Le)el

0 4 7 : ; > ?

o. of

pixels

H H 00 0 ; 0; ;

%igure 2 !3bit /istogram Equalized image

%igure ! /istogram of 0riginal !3bit image

%igure 6 /istogram of Ne+ !3bit image

-igure 7 shows the histogram of original image

-igure : shows &qualized image of the data gi)en in

Ta+le 0, Ta+le 4 and Ta+le 7. Ad)antage of histogram

equalization is that it is eas to use and

straightforward technique. "n the other hand it ma

increase the contrast of +ackground noise. /71 -igure

; is an example of nois image produced after

histogram equalization.


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%igure 7 8a9 0rignal Image 8b9 Equalized Image

Table 6 *d)antages and disad)antages

Ad)antages

#t is a fairl straightforward technique and an in)erti+le

operator. 9o in theor, if the histogram equalization

function is known, then the original histogram can +e

reco)ered. /71

$istogram equalization is a simple and effecti)e

contrast enhancement technique which distri+utes pixel

)alues uniforml such that enhanced image ha)e linear

cumulati)e histogram. /;1

#t stretches the contrast of the high histogram regions

and compresses the contrast of the low histogram

regions./;1

5ode for histogram equalization in MATLAB

e)aluates results shown in -igure ><

%igure : 8a9 Grayscale Image 8b9 Equalized

Image

$istogram equalization is +etter used for

enhancement of satellite images. 9o a 9atellite image

which is shown in -igure ? is equalized and new

image is shown in -igure 8.

%igure ; Satellite image

%igure Equalized Satellite Image

-igure H and -igure 0 shows $istograms of the

images displaed in figure ? and figure 8.

%igure ar gra#h of 8a9 0riginal Image 8b9Equalized Image

-igure 00 displas flowchart of the steps in)ol)ed in

histogram equalization.


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%igure 11 %lo+chart for /istogram Equalization

:. S#atial $omain

Most spatial domain enhancement operations can +e

generalized as<

g%x,'ET/f%x,'1.

3here, f %x, ' E the input image, g %x, ' E the

processedoutput image and T E some operator defined o)er some neigh+orhood of %x, '. 9patial

domain techniques directl deal with image pixels.

9patial techniques are particularl useful for directl

altering the gra le)el )alues of pixels and hence the

o)erall contrast of the image. But the image is

enhance in a uniform manner which sometimes

produced undesira+le results. *oint processing

operations are the simplest spatial domain operations.

-ew of them are Linear, Logarithmic and *ower law

transformations. *ixel )alues of the processed image

depend upon the pixel )alues of original image. The

expression can +e stated as g%x,' E T/f%x,'1, where

T is gra le)el transformation in point processing. />1

6-1- .inear Transformation

egati)e#dentit< This transformation re)erses the

gra le)el order. -or L gra le)els, the transformation

has the form<

s E %L!0' I r

egati)e images are useful for enhancing white or

gre detail em+edded in dark regions of an image. /01

/?15ode for egati)e transformation in Matla+

e)aluated results which are shown in -igure 04.

%igure 12 8a9 ?3ray image 8b9 Negati)e of ?3ray

image

Thresholding Transformation< it is achie)ed in a

normalized gra scale as pixel )alues of threshold

image are either Js or 0Js. #f r K threshold then s E

0. and if r E threshold then sE . where, r is the

pixel of original image and s is the pixel of

transformed image. Gesultant image is also called

+inar image. These are useful in image segmentation

to isolate an image of interest from +ack ground. /?1.

5ode of Thresholding Transformation in MATLAB

e)aluates resultsshown in -igure 07with thresholding)alue as 0.

%igure 1! 8a9 Grayscale Image of Earth 8b9

Threshold image of earth

6-2- .og Transformation

Log Transformation Technique can +e used for

contrast enhancements of dark images. The general

form is represented as


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s E c log %0 r'

3here, s is the output pixel )alue, r is the input pixel

)alue and c is a constant. /01/?1/81. 5ode for Log

Transformation in MATLAB e)aluates results shown

in -igure 0:<

%igure 16 8a9 0riginal Image 8b9 Transformed

Image

6-!- @o+er la+ transformation

*ower law transformation technique is commonl

used gra le)el transformation, in which narrow

range of input )alue are mapped into wider range of

output )alues. $ence increasing the contrast. #t can

+e represented as

s E +r ɣ

3here, s is the output pixel )alue, r is the input pixel

)alue, + is a scaling constant and is the power toɣ

which the input gra le)el is raised. Ad)antage is of

this transformation is that it is possi+le to control the

transformation function + )aring the parameter .ɣ

/01 /?1 /81 5ode of *ower law transformation

e)aluates results shown in -igure 0;.

%igure 17 8a9 0riginal Image, 8b9 Image +ith

A=-2, 8c9 Image +ith A=-6, 8d9 Image +ith A=-:ɣ ɣ ɣ

7- *nalysis of Techniques

Table 7 @arameters

*arameter =escription&fficienc #t signifies a le)el of performance

that descri+es a process that uses the

lowest amount of inputs to create

the greatest amount of outputs.

*erformance The accomplishment of a gi)en task

measured against preset known

standards of accurac, completeness,

cost, and speed.

A)aila+ilit A+ilit of technique to deli)er

ser)ices when required

Gelia+ilit A+ilit of technique to deli)er

ser)ices as specified

9afet A+ilit of technique to operate

without failure

9ecurit A+ilit of technique to protect itself

against accidental or deli+erate

intrusion

oise ndesira+le +!product of image

that adds extraneous information

9harpness 9u+2ecti)e qualit of an image

indicating clear or distinct

reproduction of detail associated

with resolution and contrast

5ontrast 9eparation +etween the darkest and

+rightest areas of the image

Table : @arametric e)aluation of Image

Enhancement Techniques

*arameters $istogram

&qualization

Linear

Transformation

Logarithmic

Transformation

*ower La

Transform

&fficienc Ner good Ner good (ood good

*erformance Ner good Ner good (ood Ner goo

A)aila+ilit (ood (ood (ood (ood

Gelia+ilit (ood Ner (ood ot good (ood

Ner good Ner good Ner good Ner goo

(ood (ood (ood (ood ot good (ood (ood (ood

9harpness (ood (ood ot good (ood

ot good (ood ot good (ood

"onclusion

=igital image processing is the use of computer

algorithms to perform image processing on digital


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images. Basicall image enhancement is the process

of ad2usting the digital images so that the resultant

images produced are more suita+le to displa and

ha)e a more )isuall pleasing effect. $istogram

equalization is +est suita+le for land images taken

from satellite, radar images and texture snthesis. #ts

code can +e used to equalize different images +ut canalso produce nois images and the histogram of

images show +alance in the num+er of pixels used.

Linear transformation can +e used for producing

negati)e of images and +lack and white images. Log

transformation is used for finding details and *ower

law can produce same image with different contrasts.

This research paper pro)ides an o)er)iew of few of

image enhancement techniques and their comparati)e

analsis.

%uture 'orB

As the requirements change with respect to time so infuture we need more efficient algorithms. The

techniques discussed a+o)e are good for image

enhancement +ut the ha)e some flaws. $istogram

equalization has equalize the image +ut it does not

reduce the noise of an image due to this the image

seems +lur so in future different algorithms will +e

implemented in order to reduce noise. The a+o)e

technique enhances onl the low contrast images, in

future, implementations will +e done for high

contrast images. #mage enhancement techniques

mention in this paper are all for the gra scale

images, such techniques can also +e implemented for color images. All the a+o)e facts in)ol)es a lot of

calculations and the are not practical for 7!=

medical image enhancement so work should +e done

for 7!= images.

4eferences

0. Gafael 5. (onzalez, Gichard &. 3oods. =igital

#mage *rocessing Third &dition

4. icholas 9ia *ik Oong, $aidi #+rahim, and 9eng5hun $oo PA Literature Ge)iew on $istogram

&qualization and #tsNariations for =igital #mage

&nhancement.Q International Journal of

Innovation, Management and Technology, Vol. 4, No. 4, ugu!t "#$%. A)aila+le<

httpSidE?

?

7. Man)i, Ga2deep 9ingh 5hauhan, Manpreet

9ingh, P #mage 5ontrast &nhancement sing

$istogram &qualizationQ International Journal

of &om'uting ( u!ine!! *e!earch. A)aila+le<

http

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Image Enhancement Techniques Final (1)