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Chapter5 Image Restoration
• Preview5 1 I d i• 5.1 Introduction
• 5.2 Diagonalizationi d i ( i fil i )• 5.3 Unconstrained Restoration( inverse filtering)
• 5.4 Constrained Restoration( wiener filtering)• 5.5 Estimating the Degradation Function• 5.6 Geometric Distortion Correction• 5.7 image inpaiting
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.1 Introduction5.1.1 Purpose
"compensate for" or "undo" defects which degrade an image.
5.1.2 Degrade Causes
( ) h i b l
co pe s e o o u do de ec s w c deg de ge.
(1) atmospheric turbulence(2) sampling, quantization p254(3) motion blur(4) camera misfocus(5) noise
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.1 Introduction5.1.3 degradation model
n(x,y)
f( ) H g(x y)f(x,y) H + g(x,y)
Assume it is a linear, position- invariant system, We can model a blurred image by
( , ) ( , )* ( , ) ( , )g x y f x y h x y n x y= +
h h( ) i ll d i d i ( )Where h(x,y) is called as Point Spread Function (PSF)
∑∑− −
+−−=1 1
),(),(),(),(M N
yxnnymxhnmfyxg2003 Digital Image Processing
Prof.Zhengkai Liu Dr.Rong Zhang3
∑∑= =
+0 0
),(),(),(),(m n
yxnnymxhnmfyxg
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5.1 Introduction5.1.4 Methods
Unconstrained Restoration: inverse filteringConstrained Restoration: wiener filtering
5.1.5 problem expression
Estimate a true image f(x,y) from a degraded image g(x,y)b d i k l d f h( ) d h i i lbased on prior knowledge of PSF h(x,y)and the statisticalproperties of noise n(x,y)
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.1 Matrix expression of degradation model: 1-D
( ) ( )* ( )g x f x h x=
( ) 0 1( )
f x x Af x
≤ ≤ −⎧= ⎨( )
0 elseef x = ⎨⎩
( ) 0 1( )
0 elsee
h x x Bh x
≤ ≤ −⎧= ⎨⎩ 0 else⎩
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.1 Matrix expression of degradation model: 1-D
1
0( ) ( ) ( ) ( )
M
e e e eg x f m h x m n x−
= − +∑ x=0 1 M-1
M=A+B-1
⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤
0m= x 0,1,…,M 1
(0) (0) ( 1) ( 1) (0) (0)(1) (1) (0) ( 2) (1) (1)
e e e e e e
e e e e e e
g h h h M f ng h h h M f n
g Hf n
− − +⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥− +⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥= + = = +⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥( -1) ( 1) ( 2) (0) ( -1) ( 1)e e e e e eg M h M h M h f M n M⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥− − −⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦ ⎣ ⎦ ⎣ ⎦
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.1 Matrix expression of degradation model: 1-D
( ) ( )h x h x M= +( ) ( )e eh x h x M+(0) ( 1) (1)e e eh h M h−⎡ ⎤
⎢ ⎥(1) (0) (2)
( 1) ( 2) (0)
e e eh h hH
h M h M h
⎢ ⎥⎢ ⎥=⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦( 1) ( 2) (0)e e eh M h M h⎢ ⎥− −⎢ ⎥⎣ ⎦
H is circulant
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.1 Matrix expression of degradation model: 2-D
( , ) 0 1 and 0 1( )
f x y x A y Bf x
≤ ≤ − ≤ ≤ −⎧= ⎨( )
0 A 1 or B 1ef xx M y N
= ⎨ ≤ ≤ − ≤ ≤ −⎩
( , ) 0 1 and 0 1( )
h x y x C y Dh x
≤ ≤ − ≤ ≤ −⎧= ⎨( )
0 A 1 or B 1eh xx M y N
= ⎨ ≤ ≤ − ≤ ≤ −⎩
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.1 Matrix expression of degradation model: 2-D
y=0 1 N-1
x=0,1,…,M-11 1
0 0( , ) ( , ) ( , )
M N
e e em n
g x y f m n h x m y n− −
= =
= − −∑∑y 0,1,…,N 1
0 1 1
1 0 2
(0) (0)(1) (1)
M e e
e e
H H H f nH H H f n
g Hf n
−⎡ ⎤ ⎡ ⎤ ⎡ ⎤⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥= + = +⎢ ⎥ ⎢ ⎥ ⎢ ⎥
1 2 0 ( -1) ( 1)M M e e
g f
H H H f MN n MN− −
⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥−⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎣ ⎦ ⎣ ⎦ ⎣ ⎦
H is block-circulant
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.1 Matrix expression of degradation model: 2-D
( 0) ( 1) ( 1)h i h i N h i−⎡ ⎤
where
( ,0) ( , 1) ( ,1)( ,1) ( ,0) ( , 2)
e e e
e e ei
h i h i N h ih i h i h i
H
−⎡ ⎤⎢ ⎥⎢ ⎥=⎢ ⎥⎢ ⎥( , 1) ( , 2) ( ,0)e e eh i N h i N h i⎢ ⎥− −⎢ ⎥⎣ ⎦
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.2 Diagonliztion: 1-D
The eigenvector and eigenvalue of a circulant matrix H are
2 2( ) 1 exp exp ( 1)T
w k j k j M kM Mπ π⎡ ⎤⎛ ⎞ ⎛ ⎞= −⎜ ⎟ ⎜ ⎟⎢ ⎥⎝ ⎠ ⎝ ⎠⎣ ⎦
g g
M M⎝ ⎠ ⎝ ⎠⎣ ⎦
2 2( ) (0) ( 1)exp (1)exp ( 1)j jk h h M k h M kπ πλ ⎛ ⎞ ⎛ ⎞= + + +⎜ ⎟ ⎜ ⎟( ) (0) ( 1)exp (1)exp ( 1)e e ek h h M k h M kM M
λ = + − − + + − −⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠
Combine the M eigenvectors to a matrix
[ (0) (1) ( 1)]W w w w M= −g
then the H can be expressed as1H WDW −= ( , ) ( )D k k kλ=
then the H can be expressed aswhere
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.2 Diagonliztion: 1-D
g Hf n= +for
1 1 1
1 1 1
W g W Hf W nW WDW f W n
− − −
− − −
= +
= +1 1DW f W n− −= +
( ) ( ) ( ) ( )G u H u F u N u+( ) ( ) ( ) ( )G u H u F u N u= +
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.2 Diagonalization 5.2.2 Diagonliztion: 2-D
2( , ) exp NW i m j im WMπ⎛ ⎞= ⎜ ⎟
⎝ ⎠
2( , ) expNW k n j knNπ⎛ ⎞= ⎜ ⎟
⎝ ⎠
1H WDW −= ( , ) ( , ) ( , ) ( , )G u v H u v F u v N u v= +
g(x,y) = f(x,y)*h(x,y) +n(x,y) in Spatial Coordinates
G(u,v) = F(u,v)H(u,v) +N(u,v) in frequency domain
g = Hf + n in vector form
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.3 Inverse Filtering5.3 Inverse Filtering5.3.1 assumption
H i i d th i i li iblH is given, and the noise is negligible
G( )
5.3.2 degradation model
F(u,v) H G(u,v)
( , ) ( , ) ( , )G u v H u v F u v=
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.3 Inverse Filtering5.3 Inverse Filtering5.3.3 restoration
( , )ˆ ( , ) ( , ) ( , )( , ) I
G u vF u v G u v H u vH u v
= =
1( , )( , )IH u v
H u v= deconvolution
5.3.4 properties ( , )ˆ ( , )( )
G u vF u vH u v
=( , )
( , ) ( , ) ( , )( , )
H u vH u v F u v N u v
H u v+
=( , )( , )( , )( , )
N u vF u vH u v
= +
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.3 Inverse Filtering5.3 Inverse Filtering5.3.4 properties
sensitive to additive noise: if H(u,v) has zero or very small value,the N(u,v)/H(u,v) could easily dominate the estimate
5.3.5 improvements p2 2 2
01/ ( , ) if ( , )
1 elseH u v u v w
M u v⎧ + <
= ⎨⎩
H(u,v)⎩
or if ( , )
( , )1/ ( , ) elsek H u v d
M u vH u v
<⎧= ⎨⎩
u,v
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.3 Inverse Filtering5.3.4 examples: without noise
Original image Blurred image Restored image
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5.3 Inverse Filtering5.3.4 examples: with noise
Blurred and noised image Restored image
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.3 Inverse Filtering5.3.4 examples: deferent cutoff
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5 4 Wiener Filtering5.4 Wiener Filtering5.4.1 assumption
H is given, and consider the image and noise as random processes
5 4 2 request5.4.2 requestThe mean square error between uncorrupted image and estimated image is minimized This error measure is given byimage is minimized. This error measure is given by
{ }{ }2 2ˆ( )e E f f= −
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5 4 Wiener Filtering5.4 Wiener Filtering5.4.3 restoration
ˆ ( , ) ( , ) ( , )WF u v G u v H u v=
Wi filt2
2
( , )1( , )( , ) ( , ) ( , ) / ( , )
Wn f
H u vH u v
H u v H u v S u v S u v= ×
+
Wiener filter, 1942
*
2( , )
( , ) ( , ) / ( , )n f
H u vH u v S u v S u v
=+ f
where2( , ) ( , )nS u v N u v= Power spectrum of the noise
2( , ) ( , )fS u v F u v= Power spectrum of the undegraded image
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5 4 Wiener Filtering5.4 Wiener Filtering5 4 5 estimate the power spectrum S (u v)= S (u v) / |H(u v)|5.4.5 estimate the power spectrum
Sn(u,v)
Sf(u,v)= Sg(u,v) / |H(u,v)|
2( , ) ( , ) ( , ) ( , )g f nS u v H u v S u v S u v= +
u,vSg(u,v)
5.4.4 properties
•optimal in terms of the mean square error
Wh H( ) 0 H ( ) 0•When H(u,v)=0, Hw(u,v)=0
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.4 Wiener Filtering5.4 Wiener Filtering5.4.5 experimental results:
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5.4 Wiener Filtering5 4 5 experimental results:5.4.5 experimental results:
The first raw:Image corrupted byImage corrupted bymotion blur andadditive noise
The second raw:R l f i fil iResults of inverse filtering
The third raw:The third raw:Results of Wiener filtering
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5 5Estimating the Degradation Function5.5Estimating the Degradation Function(blind deconvolution)
5.5.1 Estimation by image observation
(1) Choose observed sub-image ( , )sg x y(1) Choose observed sub image ( , )sg y
(2) Denote the constructed sub-image as ˆ ( , )sf x y
(3) Assume noise is negligible
thenthen( , )( , ) ˆ ( )
ss
G u vH u vF u v
=( , )sF u v
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5 5Estimating the Degradation Function5.5Estimating the Degradation Function 5.5.2 Estimation by experimentation
δ(x y) h(x y) g(x y)If f(x,y)=δ(x,y)
Impulseδ(x,y) h(x,y) g(x,y)
then( ) ( )* ( ) ( )h hδ
Impulseresponse
( , ) ( , )* ( , ) ( , )g x y x y h x y h x yδ= =
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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5 5Estimating the Degradation Function5.5Estimating the Degradation Function 5.5.3 Estimation by modeling
An atmospheric turbulence model based on the physical characteristics
Hufnagel and2 2 5 / 6( )( , ) k u vH u v e− +=
Hufnagel and Stanley, 1964
( , )where k is a constant
it has the same form as the Gaussian lowpass filter
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5.5Estimating the Degradation Function 5.5.3 Estimation by modeling
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5 5Estimating the Degradation Function5.5Estimating the Degradation Function 5.5.3 Estimation by modeling :restoration of uniform linear motion
If T is the duration of the exposure, the effect of image motionfollows that
[ ]0 00( , ) ( ), ( )
Tg x y f x x t y y t dt= − −∫
follows that
0∫
where x0(t) and y0(t) are the time varying components ofi i h di i d di i i f i
2 ( )( , ) ( , ) j ux vyG u v g x y e dxdyπ∞ ∞ − +
∞ ∞= ∫ ∫
motion in the x-direction and y-direction. Its Fourier transform is
−∞ −∞∫ ∫2 ( )
0 00[ ( ), ( )]
T j ux vyf x x t y y t dt e dxdyπ∞ ∞ − +
−∞ −∞
⎡ ⎤= − −⎢ ⎥⎣ ⎦∫ ∫ ∫2003 Digital Image Processing
Prof.Zhengkai Liu Dr.Rong Zhang29
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5 5Estimating the Degradation Function5.5Estimating the Degradation Function 5.5.3 Estimation by modeling :restoration of uniform linear motion
Reversing the order of integration:
2 ( )0 00
( , ) [ ( ), ( )]T j ux vyG u v f x x t y y t e dxdy dtπ∞ ∞ − +
−∞ −∞
⎡ ⎤= − −⎢ ⎥⎣ ⎦∫ ∫ ∫Using the translation Properties of Fourier transformation, then
0 02 [ ( ) ( )]
0( , ) ( , )
T j ux t vy tG u v F u v e dtπ− += ∫0 02 [ ( ) ( )]
0( , )
T j ux t vy tF u v e dtπ− += ∫
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5 5Estimating the Degradation Function5.5Estimating the Degradation Function 5.5.3 Estimation by modeling :restoration of uniform linear motion
0 02 [ ( ) ( )]
0( , )
T j ux t vy tH u v e dtπ− += ∫By defining
( , ) ( , ) ( , )G u v H u v F u v=thenSuppose that the image in question undergoes uniform linear
2 [ ( ) ( )]T j ux t vy tπ +∫
pp g q gmotion in the x-direction only, at a rate given by 0 ( ) /x t at T=
0 02 [ ( ) ( )]
0( , ) j ux t vy tH u v e dtπ− += ∫
2 /
0
T juat Te dtπ−= ∫0∫sin( ) j uaT ua e
uaππ
π−=
2003 Digital Image Processing Prof.Zhengkai Liu Dr.Rong Zhang
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uaπ
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5 5Estimating the Degradation Function5.5Estimating the Degradation Function 5.5.3 Estimation by modeling :restoration of uniform linear motion
If we allow the y-component to wary as well with the motiongiven by then the degradation function becomes0 ( ) /y t bt T=
( )( , ) sin[ ( )]( )
j ua vbTH u v ua vb eua vb
πππ
− += ++
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5 6 Geometric Transformation5.6 Geometric Transformation5.6.introduction
f(x,y) T g(x,y)
( , ) [( ( , )] ( ', ')g x y T f x y f x y= =
' ( , ) ' ( , )x r x y y s x y= =where
for example: zoom out ' / 2 ' / 2x x y y= =
zoom in ' 2 ' 2x x y y= =
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5 6 Geometric Distortion Correction5.6 Geometric Distortion Correction5.6.1 introduction
A geometric transformation consists of two basic operations:(1) Spatial transformation
. . . .(1) Spatial transformation
. . ..(2) Gray-level interpolation
. .空间变换
(x,y)),( ,, yx. Direct transform
f(x,y)
灰度赋值最近邻
),( ,, yxg
Inverse transform
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5 6 Geometric Distortion Correction5.6 Geometric Distortion Correction5.6.2 spatial transformations
Linear correction
1 2 3( , )( )
r x y a x a y as x y b x b y b
= + += + +
Quadratic correction
1 2 3( , )s x y b x b y b= + +
2 21 2 3 4 5 6
2 2
( , )
( )
r x y a x a y a xy a x a y a
b b b b b b
= + + + + +2 2
1 2 3 4 5 6( , )s x y b x b y b xy b x b y b= + + + + +
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5 6 Geometric Distortion Correction5.6 Geometric Distortion Correction5.6.3 gray-level interpolation
•Nearest neighbor interpolationf(i,j)
f(i j+1)( , ) ( ', ')g x y f x y=
(i j+1)
f(i,j+1)
( , )f i u j v= + +
(i,j)
(i,j+1)f(i+1,j+1), (0,1)u v∈ ,i j Z∈ v
' ( ') ' ( ')x round x y round y= =(i 1 j)
(i+1,j+1)f(i+1,j)u
(i+1,j)
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5 6 Geometric Distortion Correction5.6 Geometric Distortion Correction5.6.3 gray-level interpolation
f(x’,y’)
f(i,j)
f(i j+1)A
•Bi-linear interpolation
( ' ') ( )f f i j+ +
(i j+1)
f(i,j+1)(x’,y’)( ', ') ( , )f x y f i u j v= + +
, (0,1)u v∈ ,i j Z∈
(i,j)
(i,j+1)f(i+1,j+1)
B(1 )(1 ) ( , ) (1 ) ( , 1)
(1 ) ( 1 ) ( 1 1)u v f i j u vf i j
u v f i j uvf i j= − − + − ++ − + + + +
(i 1 j)
(i+1,j+1)f(i+1,j) (1 ) ( 1, ) ( 1, 1)u v f i j uvf i j+ + + + +
It can be operated by a mask(i+1,j)
(1 )(1 ) (1 )(1 )u v u v
u v uv− − −
−
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5 6 Geometric Distortion Correction5.6 Geometric Distortion Correction5.6.3 gray-level interpolation
Y
Rotation in 2D plane(x,y)
(x',y')
' cos( ) cos cos sin sinx r r rα θ α θ α θ= + = −(x,y)
θ
αX' sin( ) cos sin sin cosy r r rα θ α θ α θ= + = +
The original coordinated of the point in polar coordinates are:
cosx r α= siny r α=
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5 6 Geometric Distortion Correction
Notes:
5.6 Geometric Distortion Correction5.6.3 gray-level interpolation
Rotation in 2D planeNotes:
1. The origin is in the center of' cos sinx x yθ θ= −
' sin cosy x yθ θ= +
the center of image and the direction of x-y yaxis is opposite
2. The result2. The result image’s size is depended on the
x
45θ =
rotate angle
3. Interpolation is
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45θ = pneededy
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5 6 Geometric Distortion Correction5.6 Geometric Distortion Correction5.6.4 experimental results
grid Changed grid
spatial transform result
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5 6 Geometric Distortion Correction5.6 Geometric Distortion Correction5.6.4 experimental results
Spatial transform Nearest neighborhood i l i
Bi- linear interpolation
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interpolation interpolation
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Summary • Inverse filtering is a very easy and accurate way to restore an image provided that we know what the bl i filt i d th t h iblurring filter is and that we have no noise
•Wiener filtering is the optimal tradeoff between the inverse filtering and noise smoothing
It i ibl t t i ith t h i ifi• It is possible to restore an image without having specific knowledge of degradation filter and additive noise. However, not knowing the degradation filter h imposes the strictest limitationsknowing the degradation filter h imposes the strictest limitations on our restoration capabilities.
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homework
• 画出图象变质及图象恢复模型的方框图,数学公式表示及说明其物理意义。
• 列出逆滤波和维纳滤波图象恢复的具体• 列出逆滤波和维纳滤波图象恢复的具体步骤。
推导水平匀速直线运动模糊的点扩展函• 推导水平匀速直线运动模糊的点扩展函数的数学公式并画出曲线。
• 写出图像任意角度旋转算法
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The End
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