Image Processing 4-ImageEnhancement(PointProcessing).ppt

8/9/2019 Image Processing 4-ImageEnhancement(PointProcessing).ppt

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Course Website: http://www.comp.dit.ie/bmacnamee

Digital Image Processing

Image Enhancement(Point Processing)

http://www.comp.dit.ie/bmacnameehttp://www.comp.dit.ie/bmacnameehttp://www.comp.dit.ie/bmacnamee


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!2Contents

In this lecture we will loo" at imageenhancement point processing techni#ues:

$ What is point processing%

$ &egati'e images $ hresholding

$ ogarithmic transormation

$ Power law transorms $ *re+ le'el slicing

$ ,it plane slicing


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,asic patial Domain Image

Enhancement

Origin x

y Image f (x, y)

(x, y)

ost spatial domain enhancement operationscan be reduced to the orm

g (x, y) = T[ f (x, y)]

where f (x, y) is theinput image0 g (x, y) is

the processed image

and T is someoperator deined o'er

some neighbourhood

o (x, y)


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!

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!2Point Processing

he simplest spatial domain operationsoccur when the neighbourhood is simpl+ thepi1el itsel

In this case T is reerred to as a grey leveltransformation function or a point processingoperation

Point processing operations ta"e the orm

s = T ( r )

where s reers to the processed image pi1el'alue and r reers to the original image pi1el'alue


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Point Processing E1ample:

&egati'e Images

&egati'e images are useul or enhancingwhite or gre+ detail embedded in dar"

regions o an image

$ &ote how much clearer the tissue is in thenegati'e image o the mammogram below

s = 1.0 - r Original

Image

Negative

Image

I m a g e s t a " e n r o m * o n 3 a l e 3 4 W o o d s 0

D i g i t a l I m a

g e P r o c e s s i n g ( 2 5 5 2 )


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&egati'e Images (cont7)

Original Image x

y Image f (x, y)

Enhanced Image x

y Image f (x, y)

s = intensitymax - r


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hresholding

hresholding transormations areparticularl+ useul or segmentation in which

we want to isolate an ob9ect o interest rom

a bac"ground

s ;.5

5.5 r


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hresholding (cont7)

Original Image x

y Image f (x, y)

Enhanced Image x

y Image f (x, y)

s =0.0 r threshold


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!2Intensit+ ransormations


D i g i t a l I m a

g e P r o c e s s i n g ( 2 5 5 2 )


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!2,asic *re+ e'el ransormations

here are man+ dierent "inds o gre+ le'eltransormations

hree o the most

common are shownhere

$ inear

@ &egati'e/Identit+ $ ogarithmic

@ og/In'erse log

$ Power law

@ nth power/nth root I m a g e s t a " e n r o m * o n 3 a l e 3 4 W o o d s 0

D i g i t a l I m a

g e P r o c e s s i n g ( 2 5 5 2 )


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!2ogarithmic ransormations

he general orm o the log transormation is s = c log(1 ! r)

he log transormation maps a narrow range

o low input gre+ le'el 'alues into a widerrange o output 'alues

he in'erse log transormation perorms the

opposite transormation


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!2ogarithmic ransormations (cont7)

og unctions are particularl+ useul whenthe input gre+ le'el 'alues ma+ ha'e ane1tremel+ large range o 'alues

In the ollowing e1ample the Aouriertransorm o an image is put through a logtransorm to re'eal more detail

s = log(1 + r)


D i g i t a l I m a

g e P r o c e s s i n g ( 2 5 5 2 )


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;-

o

!2ogarithmic ransormations (cont7)

Original Image x

y Image f (x, y)

Enhanced Image x

y Image f (x, y)

s = log(1 ! r)

We usuall+ set c to ;

*re+ le'els must be in the range B5.50 ;.5


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;!

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!2Power aw ransormations

Power law transormations ha'e the ollowingorm

s = c r "

ap a narrow rangeo dar" input 'aluesinto a wider range ooutput 'alues or 'ice'ersa

ar+ing gi'es a wholeamil+ o cur'es


D i g i t a l I m a

g e P r o c e s s i n g ( 2 5 5 2 )


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!2Power aw ransormations (cont7)

We usuall+ set c to ;

*re+ le'els must be in the range B5.50 ;.5

Original Image x

y Image f (x, y)

Enhanced Image x

y Image f (x, y)

s = r "


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!2Power aw E1ample


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!2Power aw E1ample (cont7)

" = 0.#


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" = 0.$


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" = 0.%


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he images to theright show a

magnetic resonance

(F) image o aractured human

spine

Dierent cur'eshighlight dierent

detail

s = r 0.6

s=

r0.4

s = r 0 .


D i g i t a l I m a

g e P r o c e s s i n g ( 2 5 5 2 )


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!2Power aw E1ample


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" = &.0


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!2Power aw ransormations (cont7)

Gn aerial photoo a runwa+ isshown

his timepower lawtransorms areused to dar"en

the imageDierent cur'eshighlight

dierent detail I m a g e s t a " e n r o m * o n 3 a l e 3 4 W o o d s 0

D i g i t a l I m a g e P r o c e s s i n g ( 2 5 5 2 )

s = r .0

s=

r4.0

s = r ! .0


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!2*amma Correction

an+ o +ou might be amiliar with gammacorrection o computer monitors

Problem is that

displa+ de'ices donot respond linearl+

to dierent

intensitiesCan be corrected

using a log

transorm I m a g e s t a " e n r o m * o n 3 a l e 3 4 W o o d s 0



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!2ore Contrast Issues

I m

a g e s t a " e n r o m * o n 3 a l e 3 4 W o o d s 0


Pi i i i


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Piecewise inear ransormation

Aunctions

Father than using a well deined mathematicalunction we can use arbitrar+ userHdeinedtransorms

he images below show a contrast stretchinglinear transorm to add contrast to a poor#ualit+ image

I m




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!2*ra+ e'el licing

ighlights a speciic range o gre+ le'els $ imilar to thresholding

$ Jther le'els can be

suppressed or maintained $ Kseul or highlighting eatures

in an image

I m




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!2,it Plane licing

Jten b+ isolating particular bits o the pi1el'alues in an image we can highlight

interesting aspects o that image

$ igherHorder bits usuall+ contain most o thesigniicant 'isual inormation

$ owerHorder bits contain

subtle details

I m




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!2,it Plane licing (cont7)

I m



[10000000] [01000000]

[00100000] [00001000]

[00000100] [00000001]


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-5

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I m

a g e s t a " e n r o m * o n 3 a

l e 3 4 W o o d s 0


[10000000] [01000000]

[00100000] [00001000]

[00000100] [00000001]


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l e 3 4 W o o d s 0


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->


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Feconstructed image

using onl+ bit planes >

and 8

Feconstructed image

using onl+ bit planes >0 8

and 6

Feconstructed image

using onl+ bit planes 80 6

and I m


l e 3 4 W o o d s 0


!2


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!2ummar+

We ha'e loo"ed at dierent "inds o pointprocessing image enhancement

&e1t time we will start to loo" at

neighbourhood operations $ in particularfiltering and convolution

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Image Processing 4-ImageEnhancement(PointProcessing).ppt

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