pengolahan citra - pertemuan iii – image enhancement nana ramadijanti
DESCRIPTION
Pengolahan Citra - Pertemuan III – Image Enhancement Nana Ramadijanti Politeknik Elektronika Negeri Surabaya. Materi : Pemrosesan Piksel Histogram Gray Scale Histogram RGB Pemrosesan Piksel melalui Mapping Fungsi Pemrosesan Piksel melalui Look-up Table Brightness Contrass Gamma - PowerPoint PPT PresentationTRANSCRIPT
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Pengolahan Citra - Pertemuan III – Image EnhancementNana RamadijantiPoliteknik Elektronika Negeri Surabaya
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Materi:
1. Pemrosesan Piksel2. Histogram Gray Scale3. Histogram RGB4. Pemrosesan Piksel
melalui Mapping Fungsi
5. Pemrosesan Piksel melalui Look-up Table
6. Brightness7. Contrass8. Gamma9. Histogram Equalisasi10. Histogram Matching
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Operasi Piksel/ Transformasi Intensitas
• Ide dasar :– Operasi “Zero memory”
• Setiap output bergantung pada intensitas input pada piksel yg ada
– Memetakan warna level gray atau warna level u ke level baru v, misal: v = f ( u )– Tidak memberikan informasi baru– Tapi dapat meningkatkan penampilan visual atau membuat fitur mudah
untuk dideteksi
• Contoh-1: Transformasi Warna Koordinat– RGB setiap piksel komponen luminance + chrominance dll
• Contoh-2: Kuantisasi scalar– Kuantisasi piksel luminance/color dengan jumlah bit yg lebih sedikit
input gray level u
outp
ut g
ray
leve
l
v
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Pemrosesan Piksel
• Pemrosesan piksel adalah mengubah nilai piksel sebagai fungsi dari nilainya sendiri;
• Pemrosesan piksel tidak bergantung pada nilai piksel tetangganya.
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• Brightness dan contrast adjustment• Gamma correction• Histogram equalization• Histogram matching• Color correction.
Pemrosesan Piksel pada Citra
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Point Processing
original + gamma- gamma + brightness- brightness
original + contrast- contrast histogram EQhistogram mod
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I adalah citra grayscale. I(r,c) adalah 8-bit integer diantara 0 dan 255. Histogram, hI, dari I:
– 256-element array, hI – hI (g), untuk g = 0, 1, 2, …, 255, adalah integer – hI (g) = jumlah piksel pada I dengan nilai = g.
Histogram Citra Gray Scale
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Histogram Citra Gray Scale
16-level (4-bit) citra
Tanda hitam menunjukkan piksel dengan intensitas g
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The Histogram of a Grayscale Image
Histogram : Jumlah piksel dengan intensitas g
Piksel tanda hitam dengan intensitas g
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The Histogram of a Grayscale Image
Plot of histogram: Jumlah piksel dengan intensitas g
Piksel dengan tanda hitam with intensity g
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. graylevel with in pixels of number the
gI
ghI 1
Luminosity
The Histogram of a Grayscale Image
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If I is a 3-band image (truecolor, 24-bit) then I(r,c,b) is an integer between 0 and 255. Either I has 3 histograms:
– hR(g+1) = # of pixels in I(:,:,1) with intensity value g– hG(g+1) = # of pixels in I(:,:,2) with intensity value g– hB(g+1) = # of pixels in I(:,:,3) with intensity value g
or 1 vector-valued histogram, h(g,1,b) where– h(g+1,1,1) = # of pixels in I with red intensity value g– h(g+1,1,2) = # of pixels in I with green intensity value g– h(g+1,1,3) = # of pixels in I with blue intensity value g
The Histogram of a Color Image
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There is one histo-gram per color bandR, G, & B. Luminosity histogram is from 1 band = (R+G+B)/3
RIhGIh
BIh
LIh
Luminosity
The Histogram of a Color Image
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The Histogram of a Color Image
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Value or Luminance Histograms
The value histogram of a 3-band (truecolor) image, I, is the histogram of the value image,
crBcrGcrRcrV ,,,31,
Where R, G, and B are the red, green, and blue bands of I. The luminance histogram of I is the histogram of the luminance image,
crBcrGcrRcrL ,0.114,0.587,0.299,
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Value Histogram vs. Average of R,G,&B Histograms
R,G,B,&V histograms
V & avg. of R,G,&B histograms
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Multi-Band Histogram Calculator in Matlab% Multi-band histogram calculatorfunction h=histogram(I)
[R C B]=size(I);
% allocate the histogramh=zeros(256,1,B);
% range through the intensity valuesfor g=0:255 h(g+1,1,:) = sum(sum(I==g)); % accumulateend
return;
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Multi-Band Histogram Calculator in Matlab% Multi-band histogram calculatorfunction h=histogram(I)
[R C B]=size(I);
% allocate the histogramh=zeros(256,1,B);
% range through the intensity valuesfor g=0:255 h(g+1,1,:) = sum(sum(I==g)); % accumulateend
return;
Loop through all intensity levels (0-255)Tag the elements that have value g.The result is an RxCxB logical array that has a 1 wherever I(r,c,b) = g and 0’s everywhere else.Compute the number of ones in each band of the image for intensity g. Store that value in the 256x1xB histogram at h(g+1,1,b).
sum(sum(I==g)) computes one number for each band in the image.
If B==3, then h(g+1,1,:) contains 3 numbers: the number of pixels in bands 1, 2, & 3 that have intensity g.
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Point Ops via Functional Mappings
Input Output
I , point operator JImage:
I(r,c) function, f J(r,c)Pixel:
IJ
.,
,
kcrJkgf
gcrI
then and
If
The transformation of image I into image J is accomplished by replacing each input intensity, g, with a specific output intensity, k, at every location (r,c) where I(r,c) = g.
The rule that associates k with g is usually specified with a function, f, so that f (g) = k.
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.,
,,cr
crIfcrJ locations pixels all for
,One-band Image
Three-band Image
.,3,2,1,,,,,,,,
crbbcrIfbcrJbcrIfbcrJ
b
all and for ,or ,
Point Ops via Functional Mappings
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.,
,,cr
crIfcrJ locations pixels all for
,One-band Image
Three-band Image
.,3,2,1,,,,,,,,
crbbcrIfbcrJbcrIfbcrJ
b
all and for ,or ,
Either all 3 bands are mapped through the same function, f, or …
… each band is mapped through a separate func-tion, fb.
Point Ops via Functional Mappings
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Point Operations using Look-up Tables
1 IJ LUT
A look-up table (LUT) implements a functional mapping.
,255,,0
,
g
gfk for
If
in values on takes if and
,255,,0k
… then the LUT that implements f is a 256x1 arraywhose (g +1)th value is k = f (g).
To remap a 1-band image, I, to J :
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.3,2,11:,:,:,:,
1
bbIbJ
IJ
b for LUT b) ,LUT a) or
If I is 3-band, then a) each band is mapped separately using the same
LUT for each band or b) each band is mapped using different LUTs – one for
each band.
Point Operations using Look-up Tables
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Point Operations = Look-up Table Ops
0 127 255
012
725
5
input value
outp
ut v
alue
index value...
101102103104105106...
...646869707071...
E.g.:
input output
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cell
inde
x
cont
ents
0 0
64 32
128 128
192 224
255 255
...
...
...
...
...
...
...
...
Look-Up Tables
input output
a pixel with this value
is mapped to this value
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How to Generate a Look-Up Table
32/)127(1255;
255,,0.2
xaeax
xa
Let Let
For example:
a = 2;x = 0:255;LUT = 255 ./ (1+exp(-a*(x-127)/32));
Or in Matlab:
This is just one example.
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255,
256,255
,,,
gcrIgcrIgcrI
crJk
kkk if
if ,
Point Processes: Increase Brightness
0 127 255
012 7
25 5
g
transform mapping index. band the is and 3210 ,,kg
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Point Processes: Decrease Brightness
0 127 255
012 7
25 5
transform mapping
255-g
index. band the is and 3210 ,,kg
crI
gcrIgcrI
crJk
k
kk ,
0,,,
0,
if if
,
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Point Processes: Increase Contrast
.255,,255,0
,0,
,255,,
,0,
crTcrT
crTcrTcrJ
k
k
k
kk
if if if
0.1127127,),( acrIacrT kk where, Let
0 127 255
012 7
25 5
transform mapping 321 ,,k
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Point Processes: Decrease Contrast
.3,2,10.10
127127,),(
ka
crIacrT kk
and where
,
0 127 255
012 7
25 5
transform mapping
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Point Processes: Contrast Stretch
. ,,
Then,.,max , ,min ,,max , ,min Let
JII
IJJ
JJ
II
mmMmcrImMcrJ
crJMcrJmcrIMcrIm
0 127 255
012 7
25 5
transform mappingmI MI
mJ
MJ
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Information Loss from Contrast Adjustment
orig
lo-c
hi-c
histograms
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Information Loss from Contrast Adjustmentorig
orig
orig
lo-c hi-c
lo-c
hi-c
rest
rest
lo-c
hi-c
diff
diff
abbreviations: original low-contrast high-contrast restored difference
difference between original and restored low-contrast
difference between original and restored high-contrast
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Point Processes: Increased Gamma
0 127 255
012 7
25 5
transform mapping
0.1255
,255,/1
for crIcrJ
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Point Processes: Decreased Gamma
0 127 255
012 7
25 5
transform mappingm M
0.1255
,255,/1
for crIcrJ
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Gamma Correction: Effect on Histogram
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Distribusi Kumulatif• Distribusi kumulatif C(x) adalah nilai total histogram dari
tingkat keabuan=0 sampai dengan tingkat keabuan=x, dan didefinisikan dengan:
• Distribusi kumulatif ini dapat digunakan untuk menunjukkan perkembangan dari setiap step derajat keabuan.
• Pada distribusi kumulatif, gambar dikatakan baik bila mempunyai distribusi kumulatif yang pergerakannya hampir sama pada semua derajat keabuan.
x
w
wHxC0
)()(
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Distribusi Kumulatif
Perubahan yang tajam
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Distribusi Kumulatif
• Gambar-gambar hasil photo mempunyai perubahan yang tidak terlalu tajam dan biasanya tidak lebih dari satu. Hal ini menunjukkan tingkat gradiasi yang halus pada gambar hasil photo.
• Gambar-gambar kartun mempunya banyak perubahan yang tajam, hal ini menunjukkan tingkat gradiasi pada gambar kartun rendah (kasar).
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Histogram Equalization• Histogram Equalization adalah suatu proses untuk
meratakan histogram agar derajat keabuan dari yang paling rendah (0) sampai dengan yang paling tinggi (255) mempunyai kemunculan yang rata.
• Dengan histogram equalization hasil gambar yang memiliki histogram yang tidak merata atau distribusi kumulatif yang banyak loncatan gradiasinya akan menjadi gambar yang lebih jelas karena derajat keabuannya tidak dominan gelap atau dominan terang.
• Proses histogram equalization ini menggunakan distribusi kumulatif, karena dalam proses ini dilkakukan perataan gradien dari distribusi kumulatifnya.
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Formulasi Histogram Equalization
Histogram Equalization dari suatu distribusi kumulatif C adalah:
yx
w
nntcw
..
Cw adalah nilai distribusi kumulatif pada derajat keabuan wt adalah nilai threshold derajat keabuan= 28 atau 256nx dan ny adalah ukuran gambar.
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Perhitungan Histogram Equalization
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10 11 12
Perhatikan histogram berikut:
2 4 3 1 3 6 4 3 1 0 3 2
Distribusi Kumulatifnya
2 6 9 10 13 19 23 26 27 27 30 32
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12
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Perhitungan Histogram Equalization
w Cw w-baru
1 2
(2*12)/32
1
2 6 2
3 9 3
4 10 4
5 13 5
6 19 7
7 23 9
8 26 10
9 27 10
10 27 10
11 30 11
12 32 12
Distribusi Kumulatif: 2 6 9 10 13 19 23 26 27 27 30 32
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12
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Histogram Equalization Pada Gambar
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Histogram Equalization Pada Gambar
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Histogram Equalization Pada Gambar
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Histogram Equalization
• Histogram: diagram yang menunjukkan jumlah kemunculan grey level (0-255) pada suatu citra
• Histogram processing:– Gambar gelap: histogram cenderung ke sebelah kiri– Gambar terang: histogram cenderung ke sebelah kanan– Gambar low contrast: histogram mengumpul di suatu
tempat– Gambar high contrast: histogram merata di semua tempat Histogram processing: mengubah bentuk histogram agar pemetaan gray level pada citra juga berubah
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Histogram Equalization in all grey level and all area (1)
• Ide: mengubah pemetaan greylevel agar sebarannya (kontrasnya) lebih menyebar pada kisaran 0-255
• Sifat:– Grey level yang sering muncul
lebih dijarangkan jaraknya dengan grey level sebelumnya
– Grey level yang jarang muncul bisa lebih dirapatkan jaraknya dengan grey level sebelumnya
– Histogram baru pasti mencapai nilai maksimal keabuan (contoh: 255)
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Histogram Equalization in all grey level and all area (2)
- mengubah pemetaan grey level pada citra, dengan rumus:
citra pada ada yang maksimal levelgrey adalah L1,.....,1,010
)()(0 0
Lkdanr
rpnn
rTs
k
k
j
k
jj
jkk
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Histogram Equalization in all grey level and all area (3)
• Contoh : citra dengan derajat keabuan hanya berkisar 0-10
Citra awal: 3 5 5 5 45 4 5 4 45 3 4 4 44 5 6 6 3
Derajat Keabuan
KemunculanKunmulatif
Probabilitas Kemunculan
Sk
SK * 10
Derajat keabuan baru
0 1 2 3 4 5 6 7 8 9 100 0 0 3 8 7 2 0 0 0 00 0 0 3 11 18 20 20 20 20 20(0*10)/20 (0*1
0)/20(0*10)/20
(3*10)/20
(11*10)/20
(18*10)/20
(20*10)/20
(20*10)/20
(20*10)/20
(20*10)/20
(20*10)/20
0 0 0 0.15 0.40 0.35 0.1 0 0 0 00 0 0 0.15 0.55 0.90 1 1 1 1 1
0 0 0 1.5 5.5 9 10 10 10 10 100 0 0 1 5 9 10 10 10 10 10
Citra Akhir: 1 9 9 9 59 5 9 5 59 1 5 5 55 9 10 10 1
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Histogram Equalization specific grey level (hist. specification)
• Histogram equalization tidak dilakukan pada seluruh bagian dari histrogram tapi hanya pada bagian tertentu saja
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Histogram Equalization specific area (local enhancement)
• Histogram equalization hanya dilakukan pada bagian tertentu dari citra
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The Probability Density Function of an Image
. Let
255
0
1g
I ghAk
. then columns by rows is if is That . in pixels of number the is
, value with) image of band color th (the
in pixels of number the is since that Note
CRACRIIA
gIkI
gh
k
I k
1
Then, 1 1 1
is the graylevel probability density function of .
k kI I
k
p g h gA
I
This is the probability that an arbitrary pixel from Ik has value g.
pdf[lower case]
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• pband(g+1) is the fraction of pixels in (a specific band of) an image that have intensity value g.
• pband(g+1) is the probability that a pixel randomly selected from the given band has intensity value g.
• Whereas the sum of the histogram hband(g+1) over all g from 1 to 256 is equal to the number of pixels in the image, the sum of pband(g+1) over all g is 1.
• pband is the normalized histogram of the band.
The Probability Density Function of an Image
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The Probability Distribution Function of an Image
,
1
1 11 11 255
0
0
0 0
k
k
kkk
I
g
Ig g
III
h
hh
ApgP
This is the probability that any given pixel from Ik has value less than or equal to g.
PDF[upper case]
Let q = [q1 q2 q3] = I(r,c) be the value of a
randomly selected pixel from I. Let g be a specific graylevel. The probability that qk ≤ g
is given by
where hIk(γ +1) is
the histogram of the kth band of I.
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The Probability Distribution Function of an Image
,
1
1 11 11 255
0
0
0 0
k
k
kkk
I
g
Ig g
III
h
hh
ApgP
Let q = [q1 q2 q3] = I(r,c) be the value of a
randomly selected pixel from I. Let g be a specific graylevel. The probability that qk ≤ g
is given by
where hIk(γ +1) is
the histogram of the kth band of I.
This is the probability that any given pixel from Ik has value less than or equal to g.
Also called CDF for “Cumulative Distribution Function”.
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The Probability Distribution Function of an Image
• Pband(g+1) is the fraction of pixels in (a specific band of) an image that have intensity values less than or equal to g.
• Pband(g+1) is the probability that a pixel randomly selected from the given band has an intensity value less than or equal to g.
• Pband(g+1) is the cumulative (or running) sum of pband(g+1) from 0 through g inclusive.
• Pband(1) = pband(1) and Pband(256) = 1; Pband(g+1) is nondecreasing.
A.k.a. Cumulative Distribution Function.
Note: the Probability Distribution Function (PDF, capital letters) and the Cumulative Distribution Function (CDF) are exactly the same things. Both PDF and CDF will refer to it. However, pdf (small letters) is the density function.
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Point Processes: Histogram Equalization
1Let IP
.1,255, crIPcrJ I
Task: remap image I so that its histogram is as close to constant as possible
Then J has, as closely as possible, the correct histogram if
all bandsprocessedsimilarlyThe CDF itself is used as the LUT.
be the cumulative (probability) distribution function of I.
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Point Processes: Histogram Equalization
after
1255, gPcrJ I
before
Luminosity
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Histogram EQ The CDF (cummulative distribution) is the LUT for remapping.
CDF
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Histogram EQ
LUT
The CDF (cummulative distribution) is the LUT for remapping.
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Histogram EQ The CDF (cummulative distribution) is the LUT for remapping.
LUT
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Histogram EQ
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Point Processes: Histogram Equalization
.
1111,, J
II
IIIJJ m
mPmPcrIPmMcrJ
Task: remap image I with min = mI and max = MI so that its histogram is as close to constant as possible and has min = mJ and max = MJ .
Then J has, as closely as possible, the correct histogram if
Using intensity extrema
1Let IPbe the cumulative (probability) distribution function of I.
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Point Processes: Histogram Matching
Task: remap image I so that it has, as closely as possible, the same histogram as image J.
Because the images are digital it is not, in general, possible to make hI hJ . Therefore, pI pJ .
Q: How, then, can the matching be done?A: By matching percentiles.
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Matching Percentiles
Recall: • The CDF of image I is such that 0 PI ( gI ) 1. • PI ( gI +1) = c means that c is the fraction of pixels in I that have a
value less than or equal to gI . • 100c is the percentile of pixels in I that are less than or equal to gI .
To match percentiles, replace all occurrences of value gI in image I with the value, gJ, from image J whose percentile in J most closely matches the percentile of gI in image I.
… assuming a 1-band image or a single band of a color image.
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Matching Percentiles
If I(r,c) = gI then let K(r,c) = gJ where gJ is such that
PI (gI) > PJ (gJ -1) AND PI (gI) PJ (gJ).
So, to create an image, K, from image I such that K has nearly the same CDF as image J do the following:
gI
P I ( g
I )
gJ
P J ( g
J )
Example:I(r,c) = 5PI (5) = 0.65PJ (9) = 0.56PJ (10) = 0.67K(r,c) = 10
… assuming a 1-band image or a single band of a color image.
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Histogram Matching Algorithm
1 : CDF of
1 : CDF of . I I
J J
P g I
P g J
min , max , min , max .
J
J
I
I
m JM Jm IM I
for gI = mI to MI while
1111255
IIJJ
IIJ
gPgPgPg ANDAND
;1 JJ gg
end
end
[R,C] = size(I);K = zeros(R,C);gJ = mJ;
IJ gIgKK
This directly matches image I to image J.
Better to use a LUT. See slide 54.
… assuming a 1-band image or a single band of a color image.
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Example: Histogram Matching
Image pdf Image with16 intensityvalues
g
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Example: Histogram Matching
Image CDF
g
CD
F I(g)
*
*a.k.a Cumulative Distribution Function, CDFI.
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Example: Histogram Matching
Target pdf Target with16 intensityvalues
g
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Example: Histogram Matching
Target CDF
g
CD
F I(g)
*
*a.k.a Cumulative Distribution Function, CDFJ.
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Histogram Matching with a Lookup TableThe algorithm on slide 50 matches one image to another directly. Often it is faster or more versatile to use a lookup table (LUT). Rather than remapping each pixel in the image separately, one can create a table that indicates to which target value each input value should be mapped. Then
K = LUT[I+1]
In Matlab if the LUT is a 256 × 1 matrix with values from 0 to 255 and if image I is of type uint8, it can be remapped with the following code:
K = uint8(LUT(double(I)+1));
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LUT Creation
10
Image CDF Target CDF
LUT
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Look Up Table for Histogram Matching
for gI = 0 to 255 while AND 25511 JIIJJ ggPgP
;1 JJ ggend
end
LUT = zeros(256,1);gJ = 0;
;1 JI gg LUT
This creates a look-up table which can then be used to remap the image.
1 : CDF of ,
1 : CDF of ,
LUT 1 : Look- Up Table
I I
J J
I
P g I
P g J
g
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Input & Target CDFs, LUT and Resultant CDF
P I ( g
)
P J ( g
)
LUT (
g )
P K (
g )
g g
g g
Input Target
LUT Result
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Example: Histogram Matching
original target remapped
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Probability Density Functions of a Color Image
red pdfgreen pdf
blue pdfluminosity pdfAtlas-Mercury
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Cumulative Distribution Functions (CDF)
red CDFgreen CDF
blue CDFluminosity CDF
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Probability Density Functions of a Color Image
red pdfgreen pdf
blue pdfluminosity pdfTechnoTrousers
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Cumulative Distribution Functions (CDF)
red CDFgreen CDF
blue CDFluminosity CDF
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Remap an Image to have the Lum. CDF of Another
original target luminosity remapped
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CDFs and the LUT
Atlas-Mercury Luminosity CDF
TechnoTrousers Luminosity CDF
LUT (Luminosity) Atlas-Mercury to TechnoTrousers
Atlas-Mercury Remapped Luminosity CDF
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Effects of Luminance Remapping on pdfs
Before After
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Effects of Luminance Remapping on CDFs
Before After
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target
Remap an Image to have the rgb CDF of Another
R, G, & B remappedoriginal
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CDFs and the LUTs
Atlas-Mercury Red PDF
TechnoTrousers Red PDF
LUT (Red) Atlas-Mercury to TechnoTrousers
Atlas-Mercury RGB Remapped Red PDF
Atlas-Mercury Green PDF
TechnoTrousers Green PDF
LUT (Green) Atlas-Mercury to TechnoTrousers
Atlas-Mercury RGB Remapped Green PDF
Atlas-Mercury Blue PDF
TechnoTrousers Blue PDF
LUT (Blue) Atlas-Mercury to TechnoTrousers
Atlas-Mercury RGB Remapped Blue PDF
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Effects of RGB Remapping on pdfs
Before After
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199
9-20
07 b
y Ri
char
d Al
an P
eter
s II
Effects of RGB Remapping on CDFs
Before After
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Remap an Image:
To Have Two of its Color pdfs Match the Third
original G & B R B & R G R & G B