digital image processing- enhancement

8/10/2019 Digital Image Processing- Enhancement

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DIGITAL IMAGE PROCESSING

IMAGE ENHANCEMENT

Image Enhancement is the process of making an image moreinterpretable for a particular application(Faust1989).

Understanding of imagery Enhancements are used to make iteasier for isual interpretation and.

!he ad antage of digital imagery is that it allo"s us tomanipulate the digital pi#el alues in an image.

$lthough radiometric corrections for illumination%atmospheric influences% and sensor characteristics may bedone prior to distribution of data to the user% the image maystill not be optimi&ed for isual interpretation.

'emote sensing de ices% particularly those operated fromsatellite platforms% must be designed to cope "ith le els of target background energy "hich are typical of all conditionslikely to be encountered in routine use.


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ith large ariations in spectral response from a di erserange of targets (e.g. forest% deserts% sno"fields% "ater% etc.)no generic radiometric correction could optimally account for and display the optimum brightness range and contrast for alltargets.

!hus% for each application and each image% a customad*ustment of the range and distribution of brightness aluesis usually necessary.

Image Enhancement $lgorithms are applied to 'emotely+ensed data to impro e the appearance of an image forhuman isual analysis or for subse,uent machine analysis.

!here is no such thing as ideal or best Image Enhancement because the results are ultimately e aluated by humans "homake sub*ecti e *udgement as to "hether a gi en ImageEnhancement is useful.

The techniques to be used in Image Enhancement de end u on

1. !he user-s data

. !he user-s /b*ecti e

0. !he user-s E#pectation


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. !he user-s background

Image Enhancement !echni,ues can be grouped underfollo"ing three categories 2

'$3I/4E!'I5 E67$65E4E6! Enhancingimages based on the alues of indi idual pi#els ( oint/peration).

+ $!I$: E67$65E4E6! ; Enhancing images based on the alues of indi idual and 6eighbouring

i#els (:ocal /peration).

+ E5!'$: E67$65E4E6! Enhancing Images by transforming the alues of each pi#el on amultiband basis.


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RADIOMETRIC ENHANCEMENT

Linea! Non Linea!

4inimum; 4a#imum :ogrithmic

< :inear In erse :og

iece ise :inear E#ponential

+,uare

+,uare 'oot

5ube

5ube 'oot

$rc !angent

7istogramE,uali&ation


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7istogram4atching

CONTRAST

!he range of brightness alues present on an image isreferred to as contrast .

'emote +ensors record reflected and emitted 'adiant Flu#e#iting from the Earth-s surface material.

Ideally one material "ould reflect a tremendous amount ofenergy in a certain "a elength% "hile another material "ould

reflect much less energy in the same "a elength.

$ common problem in remote sensing is that the range ofreflectance alues collected by a sensor may not match thecapabilities of the film or color display monitor.


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"ACTORS LEADING TO LO# CONTRAST INREMOTEL$ SENSED IMAGE

3ifferent materials often reflect similar amounts of radiant flu# throught out the =isible% 6I' and 4I'

portion of E4 +pectrum.

5ultural Factors eg. eople in de eloping countriesuse natural building material( "ood% soil) inconstruction of urban areas.

+ensiti ity of the 3etector 3etectors on mostsensing systems are designed to record a relati ely"ide range of scene brightness alues(>; ??) "ithout

becoming saturated. 7o"e er ery fe" scenes arecomposed of brightness alues that utili&e the fullsensiti ity range of the detectors.


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CONTRAST ENHANCEMENT

5ontrast enhancement is a process that makes the imagefeatures stand out more clearly by making optimal use of thecolors a ailable on the display or output de ice.

5ontrast manipulations in ol e changing the range of aluesin an image in order to increase contrast.

For e#ample% an image might start "ith a range of alues bet"een > and 9>. hen this is stretched to a range of > to

??% the differences bet"een features is accentuated.

!he contrast of an image can be increased by utili&ing theentire brightness range of a display or hard;copy outputde ice.

3igital methods generally produce a more satisfactorycontrast enhancement because of the precision and "ide

ariety of processes that can be applied to the imagery.


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:inear and nonlinear digital techni,ues are t"o "idely practiced methods of increasing the contrast of an image.

LINEAR CONTRAST STRETCHING

% L inear contrast enhancement% also referred to as a contraststretching% linearly e#pands the original digital alues of theremotely sensed data into a ne" distribution.

@ Ay e#panding the original input alues of the image% the totalrange of sensiti ity of the display de ice can be utili&ed.

@ :inear contrast enhancement also makes subtle ariations"ithin the data more ob ious.

@ !hese types of enhancements are best applied to remotelysensed images "ith Baussian or near;Baussian histograms%

meaning% all the brightness alues fall "ithin a narro" rangeof the histogram and only one mode is apparent.

!here are three methods of linear contrast enhancement2


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4inimum;4a#imam :inear 5ontrast +tretch ercentage :inear 5ontrast +tretch iece"ise :inear 5ontrast +tretch

Minimum&Ma'imum Linea! Cont!ast St!etch

!he original minimum and ma#imum alues of the data areassigned to a ne"ly specified set of alues that utili&e the full

range of a ailable brightness alues.

5onsider an image "ith a minimum brightness alue of 8and a ma#imum alue of 1?0. hen such an image is ie"ed"ithout enhancements% the alues of > to 80 and 1? to ??are not displayed.

Important spectral differences can be detetected by stretching

the minimum alue of 80 to > and the ma#imum alue of 1?0to ??

$n algorithim can be used that matches the old minimumalue to the ne" minimum alue% and the old ma#imumalue to the ne" ma#imum alue.

$ll the old intermediate alues are scaled proportionately bet"een the ne" minimum and ma#imum alues.


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4any digital image processing systems ha e built;incapabilities that automatically e#pand the minimum andma#imum alues to optimi&e the full range of a ailable

brightness alues.

fig2 :inear 5ontrast stretching


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'$ 3$!$

4I6I4U4;4$CI4U4 5/6!'+! E67$65E4E6!


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Pe!centage Linea! Cont!ast St!etch

!he percentage linear contrast stretch is similar to theminimum;ma#imum linear contrast stretch e#cept thismethod uses a specified minimum and ma#imum alues thatlie in a certain percentage of pi#els from the mean of thehistogram.

$ standard de iation from the mean is often used to push thetails of the histogram beyond the original minimum andma#imum alues.

It is not necessary that the same percentage be applied toeach tail of the histogram distribution.


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$n analyst "anting to e#tract detailed marine information inan image may only be interested in alues bet"een > and 1 .

hen these alues are stretched to > and ??% subtle oceanariations can be more easily detected .

$ percentage stretch of the same image bet"een alues of ?and ? yields detailed egetation information. !his may beuseful in the delineation of healthy egetation.

If an analyst is interested in image enhancement for urbanfeatures% a percentage linear stretch bet"een the alues >and D> in the red gun and 1? to ? in the green and blue guns"ill increase the contrast of these features.

ercentage :inear 5ontrast +tretch

(a) Data *ithout Enhancements (b) Enhanced +o! Ocean
http://www.cla.sc.edu/geog/rslab/rsccnew/mod6/6-3/gif/oceanstretch.gifhttp://www.cla.sc.edu/geog/rslab/rsccnew/mod6/6-3/gif/c_normal.gif


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Aands %0% and Cont!ast

Red >;1> G!een >;1 ,-ue >;1

(b) Enhanced +o! .egetationCont!ast

Red ?; ? G!een 0>;0? ,-ue ?;0>

(c) Enhanced +o! /!banCont!ast

Red >;D>G!een 1?; ? ,-ue1?; ?

ercentage :inear +tretch
http://www.cla.sc.edu/geog/rslab/rsccnew/mod6/6-3/gif/urbanstretch.gifhttp://www.cla.sc.edu/geog/rslab/rsccnew/mod6/6-3/gif/vegstretch.gif


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Piece*ise Linea! Cont!ast St!etch

hen the distribution of a histogram in an image is bi ortrimodal% an analyst may stretch certain alues of thehistogram for increased enhancement in selected areas.

!his method of contrast enhancement is called a piece"iselinear contrast stretch.

$ piece"ise linear contrast enhancement in ol es theidentification of a number of linear enhancement steps thate#pands the brightness ranges in the modes of the histogram.

In the piece"ise stretch% a series of small min;ma# stretchesare set up "ithin a single histogram.

Aecause a piece"ise linear contrast stretch is a ery po"erfulenhancement procedure% image analysts must be ery familar"ith the modes of the histogram and the features theyrepresent in the real "orld.


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iece "ise linear stretch


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NON LINEAR CONTRAST STRETCH

36 alues are non linearly stretched to yield an enhancedimage.

/utput brightness alue is a function of input brightnessalue.

Increases contrast in different regions of histogram

!here are basically t"o types of 6on linear enhancementtechni,ues 2

4$!7E4$!I5$: ; log% in erse log%s,uare%s,uare root% cube% cube root etc.

+!$!I+!I5$: 7istogram E,uali&ation etc.


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:ogrithmic +tretch

In this process the logarithmic alues of the input data arelinearly stretched to get the desired output alues.

It is a t"o step process. In the first step "e find out the logalues of the input 36 alues. In the second step the logalues are linearly stretched to fill the complete range of 36

no. (>; ??). :ogrithmic stretch has greatest impact on the brightness

alues found in the darker part of the histogram


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Histog!am Equa-i0ation

7istogram e,uali&ation is one of the most useful forms of nonlinear contrast enhancement.

hen an image s histogram is e,uali&ed% all pi#el alues of the image are redistributed so there are appro#imately ane,ual number of pi#els to each of the user;specified outputgray;scale classes (e.g.% 0 % % and ? ).

5ontrast is increased at the most populated range of brightness alues of the histogram (or GpeaksG).

It automatically reduces the contrast in ery light or dark parts of the image associated "ith the tails of a normallydistributed histogram (Hensen 199 ).

7istogram e,uali&ation can also separate pi#els into distinctgroups% if there are fe" output alues o er a "ide range.


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7I+!/B'$4 E U$:IJE3 I4$BE


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Enhanced image gains contrast in peaks

3ata alues in the tails of the original histogram are groupedtogether so contrast among tail pi#els is lost.

Image analysts must be a"are that "hile histogram

e,uali&ation often pro ides an image "ith the most contrastof any enhancement techni,ue% it may hide much neededinformation.

!his techni,ue groups pi#els that are ery dark or ery brightinto ery fe" gray scales.

If one is trying to bring out information about data in terrainshado"s% or there are clouds in your data% histograme,uali&ation may not be appropriate.


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Histog!am Matching

It is the process of determining a look up table that "illcon ert the histogram of one image to resemble thehistogram of the other.

7istogram matching is useful for matching data of the sameor ad*acent scenes that "ere scanned on separate dates% or areslightly different because of sun angle or atmospheric effects.

!his is specially useful for mosaicking or change detection.

!o achie e good results "ith histogram matching the t"oinput images should ha e similar characterstics.

1. Beneral shape of the histogram cur es should besimilar.

. 'elati e dark and light features in the image should besame.

0. For some applications% the spatial resolution of the datashould be the same.

. !he relati e distributions of the landco ers should bethe same.

?. 5louds should be remo ed before matching thehistogram.


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ercentage :inear 5ontrast +tretch

digital image processing- enhancement

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