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
8/9/2019 Image Processing 4-ImageEnhancement(PointProcessing).ppt
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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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!2
,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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!
o
!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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Point Processing E1ample:
&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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Point Processing E1ample:
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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!2
Point Processing E1ample:
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
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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;5
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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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;;
o
!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)
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 (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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;!
o
!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
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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;
o
!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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;6
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!2Power aw E1ample
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!2Power aw E1ample (cont7)
" = 0.#
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!2Power aw E1ample (cont7)
" = 0.$
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!2Power aw E1ample (cont7)
" = 0.%
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!2Power aw E1ample (cont7)
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 .
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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2;
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!2Power aw E1ample
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!2Power aw E1ample (cont7)
" = &.0
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2-
o
!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!
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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
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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!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
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 )
Pi i i i
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!2
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
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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!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
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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!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
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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2?
o
!2,it Plane licing (cont7)
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 )
[10000000] [01000000]
[00100000] [00001000]
[00000100] [00000001]
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-5
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!2,it Plane licing (cont7)
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 )
[10000000] [01000000]
[00100000] [00001000]
[00000100] [00000001]
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-;
o
!2,it Plane licing (cont7)
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-2
o
!2,it Plane licing (cont7)
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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--
o
!2,it Plane licing (cont7)
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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-!
o
!2,it Plane licing (cont7)
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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-
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!2,it Plane licing (cont7)
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 )
-6
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-6
o
!2,it Plane licing (cont7)
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 )
-8
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-8
o
!2,it Plane licing (cont7)
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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->
o
!2,it Plane licing (cont7)
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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-?
o
!2,it Plane licing (cont7)
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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!;
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!;
o
!2,it Plane licing (cont7)
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
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 )
!2
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!2
o
!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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