Qexplan ampling images UT
magbamplin th aelt a cenetng
a continubus imag ito a dischite tam by selecting
apeciie pont pixela)um thuimage
epsLeserted' asa
puocesuingwhHA QN image
grid pnel.
-Ortd ovettay:
]wWorkingidtepA gnd eplaced auH
athe continuDue inage dividingit into
imall ugula
sLctiens
btep 2- Piyel heleetioni teach
Linteuseetion ot qid ines, csampla a poirt
pixel )s chisen
4bampling ate s toaloww, then
detzilaelest
titis too high it inteaes storaqe
nd psteLsing uiments.
OHeuentiattbw pcint puseeuLing lk
ncighbouh sod pCesLing techniLel
Point puacesing Nefghbouuhoadt
pueeLLing.
1Opeate
|OReuati on singlaQetattsona
a gLeLup
pixel at actimne C pixe tomodify
modihying ite
linttnaity indopendanibuBUNding
sesapatial
ranstmation ilttng orconVolutin
uncion with knl./mask.
31Ltpeed is hastbpecd iu alou
Applications:- Applicatians i-c
ontrat strechingmeathing, edge
intensity transormlt 1 shanpening
dettctien,
ttt
plain quantization s4 animagr
marping continLpu amplitude yales
o a csampled tiqnal intn tinite sct
HOY called quantizaton eHuo
intertY uee in an imaoe,
making it
tuage ktranamssien
2)QLentization fuoe
actual inttnuityvalus uantizad ValuSInce
quantizoation involueA HDundina.Dme into is lokt
LIoading to eHO4
B) Siqna-to-Quantizatiop Nolse ROtio 6QNR)
gt menkWA thequality al quartization t
LDmpaing ength af'sginal signal t noise
introduce oy qartization..
Explain Hol o, degmertation in imqpHDCaling
1seqmertation involue aivialing imag into
ou sbjeA 4'helpin
maaningtulegien
analyzin4,undut anding & eztracting imp ingo
eom imbgeu.
object detectien
isblates ebjectsin an
econition
image o4
'4t identities
tustheu analyi
&
Featue etactipn & analyis tatacts
specieateake cdges,tettutee & shapee
fem oiht egion, 4ouclasification
a1 Smgcompuusi onelps in ieducing data
SIZ by ocusing on Holitat egions
psinung Gng Quality enhances imnga ky
Alpeuating aejnind
i ekjtcta
ehyectu backnundjem
cene undnetanding i enablés AL mahine
SienyGtems to igteup Hat an envirDnmnart
campa TIEF, JPEG
JPE
BP
2
G 8MP.
ZFeates
Eul fornm Tagged mage Joint phatagraphd Bitmap
JEile format expets qroup
]Comprekupporti beth|oy ncompused
ss9n lOsalebs lzIP)compiussion
K Louuy tompre (reduces file
Size but sacnij
quality)
ood 'quality Veuy high
hualiy quaidy lquality
bnall
Lange Venyasgo
Size
s1Commorn Projessional web imags lindous
Applicat"\photbgraphy. qraphitk
a Bst 'Archival Onine wWindow!
Used yorsto4age qraphicsicons
shaing
reLoUHCUs
UT
Qplain contuast slching
jcontuast streching af image
enhancenant metho ushich attempt to
imploue a,image by ttching the
Lange o aintcnity vales.
144 impioues the vieibulity o detaila in
image that appeau du
619t idbehul in medical imaging,
Lemote enbng A computeu vision
btraiqht- in nto map pel valee.
tom input range to tullange
i) PieceuisL dineau Sttdchin 'Dehnes.
ditt ineau unctionu Hou aigt intensity
Hange
3) Non-lin caH CS- laes Logarithmicor
eeponetial yntoenhance contraut
eleetiely.
uhat ie gama ranhopt
image nhancemont tchniqu
Ihe biiqhttness &cotat an inogt
AitheNent deuiceu k display
I Snpt pielinsity sOutput pinl
inttnaity
cOnstant
Ygamma value ,cbcalng
a1 Yvalu detenie wshcthau imagebicoml
Liqhte4, YI 9mage getu deken
6] tot Appications ) edical Inaqing
imptoues Visibility inxMAy
x-a and MRI
SCans
2) Remste bensing :- onhancos detailu in
satellite
images
3)Cemputtu vsi on - PHepae images u
gbject aletectien & MImodals
Sxplain &cbasic quay leuel
tunctins. trantoumation
GHayleuel
to onanipulatt
tanslomation
the"ntenaity
functions ae ubed
to nhanee vals o an image
te appeauane et exttact uselul
infomation.
aineaH
The 3 types ae:
tanstournation 1t includes idortit
negatiue trantomation.
n idertity ach vale o4 input mage is
-directly mappod t0 each atho valuo ol
DLtpt image
a Negati ve tranotmationt inuttathe
identity
anmatien
Hete Žach vale oy inut image id bubttacted
Lom the L-L mappd ento uteut image
s]Formia founagatiutrajomaiionis
6)Zngauthmic TAansfoumtien
1Auing thiu the an
dauk izelu in
mage a eapanded as CDmpae to
higho pixel Valuee
compessing
Types-
of
lag'k ineLu)
qyLogJeue'lu in imag.
nthHOt transomatisn.
sThse ttanA}otatiens can be qiuen
by expLLbLiDN. iS=C:yY
unhancemrtt o4images
IVP
feyoLIm Gmag niqatiue tou tollauing s BfPimg
100
135
90885
J0210S 99
Formla
we know L=2=2=256
S=256-|- 100=1S5 92= 255-H0F 14S
Ss = S S6= |1S
S+= o Sg=120 Sq== l66 SiD = l6s
|40
Neqative mq vclLes
IS4 15 |0120
166 l6S 64
140
feuyaM gag nagatiue en tall S8PP
seRp image
26 21 29 30
14 2| 20 30
26 31
27 23
ss qiven.image is a) 5 bitd L 2^=25=3 2
Formlla
S3-Y
2
12
151S 5
123
2
img
23JbitL= 20=2'
is c 3 g
qiven
s(Lsint im nagatíue transeHMation.
S=L-1=S-1-Y
S= -Y
o
2
2
2
QPeyeLm dmage Thcsholding tau el
2. ÉPimage uith T, =4
4
30
5
3 2
48
ineimage is 3bits LE 2n= 2 =8
For Threshlding 9=5L-I otherlewise
Hee L-IE 8-1F67T= 4
othewise
C 6 6 6
40
0
Lat histeqam
l
o foll img.fesyoun Hstogram
quali'zatson 'plot eguálizad' his togram
2
2
Hstogiam equalzod image.
.3
2
2
2 S 2
2 2
Gray leuels.
No d pixels 2
232 2
6
2
Histoqram i
23 5
TNewqra
Constucting table leuel
SK G+X7 Hjstogram
level pixelsln
2
)(PDE)
0:08
(CDF)
0:S6
Equallkatn
024 2
2 O24 D y8 3-36 3
2 008 392
2
0:16 072
56
5-04 S
6 3 0:12 0:92
2 0 08
n=2
3 4
Cray leuels
No. of pixels
2
2
G2 2
ualizad
Histogram
2
2
Histoqram
eqallizad
2
2
67
Iaage ST S 3
3
feuyom hist agam analyiul duaw suiqinal k
egualizad
Gayleuels
histogam
22 3 S 6
2uO |2S 83
No o pixela.
Hitoqram i
4B0lo24
1000+ ) 8S S 6SD 335
-600
S00
400
900
200
+00
No.o SK
Cnay (PDE) (CDE)
louel pixolnr) LeLels
780 0-190
TO24 O2 3:08
O2L 0-6s S
3 6SD O:81
S
6
335
240
125
O-D82
00S8
O 031 098
0-892 624
83 002
Grayleyel
No.o, pils
35
180 10248Sr 98s448
Equalzed
HiŠtDqram 800
200
OLot V be set qrayeela Cemete Du D8a
Dm diSiances
2
2p) 2 3
2S
32 2
4 2 3
Du
p=(2.4)
=-S|+
(0,o)q
Ly-t
ls,t) (4, y)
8 wnit
qutting C0mplted blu)_p Ra tho dlstane
Dm cannotbe calculated
T=3
Coretrast
r = 5 sl=9 2=6 elam
streching toL3 BPP image
Forma x (Smax Sain + Smin
Imax- Imin
Hoe
Smin = S=2 Smnax = S2= 6
For r=3S= (8-8)x(6-2)+ 2
(6-3)
2
(5-3)
2
=5-
s 3)x 6-2)+
(5-3)
2
2 x4+2
2 =G
Final mappings
Oriqinal inienslty Lr) TYansformed
3 2 intensityis)
