perbaikan citra
DESCRIPTION
PERBAIKAN CITRA. Introduction. Apa itu perbaikan citra ? Proses peningkatan kualitas visual dari citra karena proses pengambilan gambar yang tidak ideal. (Tidak fokus, gerakan blurring, illumminasi yang jelek, dll) Menilai kualitas visual citra. A Plague in Image Processing: Blur. - PowerPoint PPT PresentationTRANSCRIPT
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PERBAIKAN CITRA
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EE465: Introduction to Digital Image Processing 2
Introduction
Apa itu perbaikan citra? Proses peningkatan kualitas visual dari citra
karena proses pengambilan gambar yang tidak ideal. (Tidak fokus, gerakan blurring, illumminasi yang jelek, dll)
Menilai kualitas visual citra
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EE465: Introduction to Digital Image Processing 3
A Plague in Image Processing: Blur Dari mana blur itu berasal?
Optical blur: kamera out-of-focus Motion blur: Kamera bergerak
Mengapa perlu deblurring? Mengganggu secara visual Target yang salah untuk kompressi Jelek untuk dianalisa
Numerous applications in astronomical imaging, biomedical imaging, biometrics ...
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EE465: Introduction to Digital Image Processing 4
Restoration Images
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EE465: Introduction to Digital Image Processing 5
Another Example
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The Real (Optical) Solution
Before the repair After the repair
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Application (II): Medical Image Deblurring (Deconvolution)
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Application (III): Law Enforcement
Motion-blurred license plate image
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Restoration Example
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A Grand Challenge in Iris Recognition
out-of-focus iris image
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EE465: Introduction to Digital Image Processing 11
h(m,n) +x(m,n) y(m,n)
),( nmw
• Linear degradation model
),( nmh blurring filter
),0(~),( 2wNnmw additive white Gaussian noise
Modeling Blurring Process
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EE465: Introduction to Digital Image Processing 12
2
2
10log10w
zBSNR
Blurring SNR
The Curse of Noise
h(m,n) +x(m,n) y(m,n)
),0(~),( 2wNnmw
z(m,n)
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h(m,n): 1D horizontal motion blurring [1 1 1 1 1 1 1]/7
BSNR=40dB
Image Example
BSNR=10dBx(m,n)
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EE465: Introduction to Digital Image Processing 14
Mengapa butuh metode non linier ? Proses pemodelan degradasi citra dengan
linear system muncul terutama karena dapat dinyatakan secara matematis
Tetapi ada fenomena dalam visualisasi dan pencitraan secara fisik sulit dijelaskan degna persamaan sederhana secara linier Examples: relationship between illumination and
luminance on a complex surface, quantization of intensity values, Gamma-correction in display devices
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Point Operations Overview
Point operations are zero-memory operations wherea given gray level x[0,L] is mapped to anothergray level y[0,L] according to a transformation
)(xfy
L
L
x
y
L=255: for grayscale images
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Lazy Man Operation
L
L
x
y
xy
No influence on visual quality at all
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Digital Negative
xLy
L x0
L
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Contrast Stretching
Lxbybx
bxayax
axx
y
b
a
)(
)(
0
L x0 a b
ya
yb
200,30,1,2,2.0,150,50 ba yyba
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Clipping
Lxbab
bxaax
ax
y
)(
)(
00
L x0 a b
2,150,50 ba
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Range Compression
)1(log10 xcy
L x0
c=100
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Kesimpulan operasi titik
Pemetaan f(x) mengahasilkan hasil perbaikan yang berbeda beda
Bagaimana memilih fungsi f(x) yang sesuai untuk sembarang citra?
One systematic solution is based on the histogram information of an image Histogram equalization and specification
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Histogram based Enhancement
Histogram of an image represents the relative frequency of occurrence of various gray levels in the image
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MATLAB function >imhist(x)
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Why Histogram?
Histogram information reveals that image is under-exposed
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0
0.5
1
1.5
2
2.5
3
3.5
4
x 104
It is a baby in the cradle!
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Another Example
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0
1000
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7000
Over-exposed image
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How to Adjust the Image?
Histogram equalization Basic idea: find a map f(x) such that the histogram
of the modified (equalized) image is flat (uniform). Key motivation: cumulative probability function
(cdf) of a random variable approximates a uniform distribution
x
t
thxs0
)()(Suppose h(t) is the histogram (pdf)
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Histogram Equalization
x
t
thLy0
)(Uniform
Quantization
L
t
th0
1)(Note:
L
1
x
t
ths0
)(
x
L
y
0
cumulative probability function
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MATLAB Implementation
function y=hist_eq(x)
[M,N]=size(x);for i=1:256 h(i)=sum(sum(x= =i-1));End
y=x;s=sum(h);for i=1:256 I=find(x= =i-1); y(I)=sum(h(1:i))/s*255;end
Calculate the histogramof the input image
Perform histogramequalization
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Ic. 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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Ic. 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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Ic. 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 L
1,.....,1,010
)()(0 0
Lkdanr
rpn
nrTs
k
k
j
k
jj
jkk
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Ic. 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
Kemunculan
Probabilitas Kemunculan
Sk
SK * 10
Derajat keabuan baru
0 1 2 3 4 5 6 7 8 9 10
0 0 0 3 8 7 2 0 0 0 0
0 0 0 0.15 0.40 0.35 0.1 0 0 0 0
0 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 10
0 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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Ic. 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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Image Example
before after
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Histogram Comparison
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500
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before equalization after equalization
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Application (I): Digital Photography
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Application (II): Iris Recognition
before after
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Application (III): Microarray Techniques
before after
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Frequency-Domain Techniques (I): Unsharp Masking
0),,(),(),( nmgnmxnmy
g(m,n) is a high-pass filtered version of x(m,n)
• Example (Laplacian operator)
)]1,()1,(
),1(),1([4
1),(),(
nmxnmx
nmxnmxnmxnmg
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MATLAB Implementation
% Implementation of Unsharp masking
function y=unsharp_masking(x,lambda)
% Laplacian operationh=[0 -1 0;-1 4 -1;0 -1 0]/4;dx=filter2(h,x);y=x+lambda*dx;
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1D Example
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x(n) xlp(n)
g(n)=x(n)-xlp(n) )()()( ngnxny
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2D Example
>roidemoMATLAB command
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Frequency-Domain Techniques (II): Homomorphic filtering
),(),(),( yxryxiyxf
Illumination(low freq.)
reflectance(high freq.)
Basic idea:
),(ln),(ln),(ln yxryxiyxf
freq. domain enhancement
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Image Example
before after
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Summary of Nonlinear Image Enhancement Understand how image degradation occurs first
Play detective: look at histogram distribution, noise statistics, frequency-domain coefficients…
Model image degradation mathematically and try inverse-engineering
Visual quality is often the simplest way of evaluating the effectiveness, but it will be more desirable to measure the performance at a system level Iris recognition: ROC curve of overall system Microarray: ground-truth of microarray image segmentation
result provided by biologists