the fractional fourier transform and its applications
DESCRIPTION
The Fractional Fourier Transform and Its Applications. Presenter: Pao-Yen Lin Research Advisor: Jian-Jiun Ding , Ph. D. Assistant professor Digital Image and Signal Processing Lab Graduate Institute of Communication Engineering National Taiwan University. Outlines . Introduction - PowerPoint PPT PresentationTRANSCRIPT
The Fractional Fourier Transform and Its
ApplicationsPresenter:
Pao-Yen LinResearch Advisor:
Jian-Jiun Ding , Ph. D.Assistant professor
Digital Image and Signal Processing LabGraduate Institute of Communication Engineering
National Taiwan University
Outlines • Introduction • Fractional Fourier Transform (FrFT)• Linear Canonical Transform (LCT)• Relations to other Transformations• Applications
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Introduction• Generalization of the Fourier
Transform• Categories of Fourier Transform
a) Continuous-time aperiodic signal b) Continuous-time periodic signal (FS)c) Discrete-time aperiodic signal (DTFT)d) Discrete-time periodic signal (DFT)
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Fractional Fourier Transform (FrFT)• Notation
• is a transform of
• is a transform of
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T f x F u
F T f T f x F u
F T f
Fractional Fourier Transform (FrFT) (cont.)• Constraints of FrFT①Boundary condition
②Additive property
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0T f x f u
1T f x F u
T T f x T f x
Definition of FrFT• Eigenvalues and Eigenfunctions of
FT
• Hermite-Gauss Function
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exp 2f x F v f x i vx dx
2 24 2 1 / 2 0f x n x f x
1 4
22 2 exp2 !
n nnx H x x
n
Definition of FrFT (cont.)• Eigenvalues and Eigenfunctions of
FT
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,n n nx x 2in
n e
0
,n nn
f x A x
n nA x f x dx
2
0
e inn n
n
f x A x
Definition of FrFT (cont.)• Eigenvalues and Eigenfunctions of
FrFTUse the same eigenfunction but α order
eigenvalues
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2i nn nx e x
2
0
i nn n
n
f x x A e x
Definition of FrFT (cont.)• Kernel of FrFT
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,f x x B x x f x dx
0
1 2 2 2
2
0
,
2 exp
2 22 !
n n nn
i n
n nnn
B x x x x
x x
e H x H xn
Definition of FrFT (cont.)
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2 2
cot cot csc2 21 cot
if is not a multiple of 2
if is a multiple of 2
if + is a multiple of 2
,
u tj j jutje x t e dt
t
t
X u x t K t u dt
Properties of FrFT• Linear.• The first-order transform
corresponds to the conventional Fourier transform and the zeroth-order transform means doing no transform.
• Additive.
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Linear Canonical Transform (LCT)• Definition
where
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2 2
, , ,, , ,
2 21 for 02
a b c dF a b c d
j d j j au ut tb b b
O g t G u
e e e g t dt bj b
2
, , , 2 for 0cdj ua b c d
FO g t de g du b
1ad bc
Linear Canonical Transform (LCT) (cont.)• Properties of LCT1.When , the LCT becomes
FrFT.2.Additive property
where
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cos sinsin cos
a bc d
2 2 2 1 1 1 1, , , , , , , , ,da b c d a b c d e f g hF F FO O g t O g t
2 2 1 1
2 2 1 1
a b a be fc d c dg h
Relation to other Transformations• Wigner Distribution• Chirp Transform• Gabor Transform• Gabor-Wigner Transform• Wavelet Transform• Random Process
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Relation to Wigner Distribution• Definition
• Property
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,
2 2 exp 2
W f x W x v
f x x f x x j vx dx
2,f x W x v dv
2,F v W x v dx
Total energy ,f x W x v dxdv
Relation to Wigner Distribution
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x
x
2
f u
Relation to Wigner Distribution (cont.)• WD V.S. FrFT
• Rotated with angle
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, cos sin , sin cosf fW x v W x v x v
Relation to Wigner Distribution (cont.)• Examples
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exp 2 ,c cf x j v x W x v v v
,f x x c W x v x c
22 1 0
2 1
exp 2 2
,
f x j b x b x b
W x v b x b v
slope= 2b
v
x
1b
Relation to Chirp Transform• for
Note thatis the same as rotated by
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0 0 0cf x x x
1 2
2 20 0
ˆexp 4 2
sin
exp cot 2 csc cotc c
jf x
j x x x x
0 1 0 1 0, cos sin cW x x x x x
0 0cx x
Relation to Chirp Transform (cont.)
• Generally,
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0 0 0 0f x f x f x x x dx 0 0f x f x x x dx
f x f x x x dx
,f x f x B x x dx
Relation to Gabor Transform (GT)• Special case of the Short-Time
Fourier Transform (STFT)• Definition
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2( ) ( )
2 2, 1/2t tj
fG t e e f d
Relation to Gabor Transform (GT) (cont.)• GT V.S. FrFT
• Rotated with angle
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, cos sin , sin cosF fG u v G u v u v
Relation to Gabor Transform (GT) (cont.)• Examples
(a)GT of (b)GT of (c)GT of (d)WD of
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-10 0 10-10
0
10
-10 0 10-10
0
10
-10 0 10-10
0
10
-10 0 10-10
0
10
s t r t f t
2
22
exp 10 3 for 9 1, 0 otherwise,
exp 2 6 exp 4 10
s t jt j t t s t
r t jt j t t
f t s t r t
f t
GT V.S. WD• GT has no cross term problem
• GT has less complexity
• WD has better resolution• Solution: Gabor-Wigner Transform
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2exp 2 0.0001 when 4.2919x x
Relation to Gabor-Wigner Transform (GWT)• Combine GT and WD with arbitrary
function
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,p x y
, ( , ), ( , )f f fC t p G t W t
Relation to Gabor-Wigner Transform (GWT) (cont.)• Examples1.In (a) 2.In (b)
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, ( , ) ( , )f f fC t G t W t
2, min ( , ) , ( , )f f fC t G t W t
-10 0 10-10
-5
0
5
10
-10 0 10-10
-5
0
5
10(a) (b)
Relation to Gabor-Wigner Transform (GWT) (cont.)• Examples3.In (c) 4.In (d)
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, ( , ) ( , ) 0.25f f fC t W t G t
2.6 0.6( , ) ( , ) ( , )f f fC t G t W t
-10 0 10-10
-5
0
5
10
-10 0 10-10
-5
0
5
10(c) (d)
Relation to Wavelet Transform• The kernels of Fractional Fourier
Transform corresponding to different values of can be regarded as a wavelet family.
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2 2
2
1 2
exp sinsec
exptan
yg y f C j y
y xj f x dx
secy x
Relation to Random Process• Classification 1.Non-Stationary Random Process2.Stationary Random ProcessAutocorrelation function, PSD are
invariant with time t
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Relation to Random Process (cont.)• Auto-correlation function
• Power Spectral Density (PSD)
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, 2 2GR u E G u G u
, 2 , , jg g gS t FT R t R t e d
Relation to Random Process (cont.)• FrFT V.S. Stationary random process
• Nearly stationary
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cot
sin cos, ,cos cos
uju j
G geR u e R
, sec secG gR u R
arg , tanGR u u
cos 0
Relation to Random Process (cont.)• FrFT V.S. Stationary random process for
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cos 0
, , when 2 1 2G gR u S u H
, , when 2 3 2G gR u S u H
Relation to Random Process (cont.)• FrFT V.S. Stationary random process PSD:
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, sin cos , when cos 0G gS u v S u v
, , when cos 0G gS u v S u
Relation to Random Process (cont.)• FrFT V.S. Non-stationary random
process Auto-correlation function
PSD
rotated with angle
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2 2 33
cot csc cotcsc 2 22 3 2 3, 2,
4 sin
jju j t t tjutG g
eR u e e e R t t dt dt
, cos sin , sin cosG gS u v S u v u v
Relation to Random Process (cont.)• Fractional Stationary Random Process If is a non-stationary random process but is stationary and the autocorrelation
function of is independent of , then we call the -order fractional stationary random process.
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g t
G u
G u u g t
Relation to Random Process (cont.)• Properties of fractional stationary
random process1.After performing the fractional filter, a white
noise becomes a fractional stationary random process.
2.Any non-stationary random process can be expressed as a summation of several fractional stationary random process.
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Applications of FrFT• Filter design• Optical systems• Convolution • Multiplexing • Generalization of sampling theorem
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Filter design using FrFT• Filtering a known noise
• Filtering in fractional domain
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signal
noise
noise
u
x
u
Filter design using FrFT (cont.)• Random noise removal If is a white noise whose
autocorrelation function and PSD are:
After doing FrFT
Remain unchanged after doing FrFT!
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g t
,gR gS
,GR gS
Filter design using FrFT (cont.)• Random noise removal
• Area of WD ≡ Total energy
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signal
x
u
signal
x
u
Optical systems• Using FrFT/LCT to Represent Optical
Components• Using FrFT/LCT to Represent the
Optical Systems• Implementing FrFT/LCT by Optical
Systems
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Using FrFT/LCT to Represent Optical Components1. Propagation through the cylinder
lens with focus length
2. Propagation through the free space (Fresnel Transform) with length
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f
z
1 02 1
a bc d f
1 20 1
a b zc d
Using FrFT/LCT to Represent the Optical Systems
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input output
2f1f
0d
0
1 2
0 2 0
00 1
1 2 1 2
1 0 1 01 22 1 2 10 1
1 2 2 1 1 1
a b df fc d
d f d
d d ff f f f
Implementing FrFT/LCT by Optical Systems• All the Linear Canonical Transform can be
decomposed as the combination of the chirp multiplication and chirp convolution and we can decompose the parameter matrix into the following form
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1 0 1 01
if 01 1 1 10 1
a b bb
d b a bc d
1 01 1 1 1 if 0
10 1 0 1a b a c d c
cc d c
Implementing FrFT/LCT by Optical Systems (cont.)
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input output
1f
0d 1d
The implementation of LCT with 1 cylinder lens and 2 free spaces
input output
2f1f
0d
The implementation of LCT with 2 cylinder lenses and 1 free space
Convolution • Convolution in domain
• Multiplication in domain
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g x g f h f x h x
g f h
g x g f h f x h x
g f h
Convolution (cont.)
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+1+1 +1 +1 +1
-1-1 -1 -1 -1
+1+1 +1 +1 +1
-1-1 -1 -1 -1
f h f h f h
f h f h f h
f h f h f h
f h f h f h
1 1
1 1
f h f h f h
f h f h f h
Multiplexing using FrFT
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x
u
TDM FDM
x
u
Multiplexing using FrFT
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x
u
Inefficient multiplexing Efficient multiplexing
x
u
Generalization of sampling theorem• If is band-limited in some
transformed domain of LCT, i.e.,
then we can sample by the interval as
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f t
, , , 0, for and for some value of , , ,a b c dF u u a b c d
f t
b
Generalization of sampling theorem (cont.)
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u
x
u
Conclusion and future works • Other relations with other
transformations• Other applications
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References [1] Haldun M. Ozaktas and M. Alper Kutay, “Introduction to the Fractional Fourier Transform
and Its Applications,” Advances in Imaging and Electron Physics, vol. 106, pp. 239~286.[2] V. Namias, “The Fractional Order Fourier Transform and Its Application to Quantum
Mechanics,” J. Inst. Math. Appl., vol. 25, pp. 241-265, 1980.[3] Luis B. Almeida, “The Fractional Fourier Transform and Time-Frequency
Representations,” IEEE Trans. on Signal Processing, vol. 42, no. 11, November, 1994.[4] H. M. Ozaktas and D. Mendlovic, “Fourier Transforms of Fractional Order and Their
Optical Implementation,” J. Opt. Soc. Am. A 10, pp. 1875-1881, 1993.[5] H. M. Ozaktas, B. Barshan, D. Mendlovic, L. Onural, “Convolution, filtering, and
multiplexing in fractional Fourier domains and their relation to chirp and wavelet transforms,” J. Opt. Soc. Am. A, vol. 11, no. 2, pp. 547-559, Feb. 1994.
[6] A. W. Lohmann, “Image Rotation, Wigner Rotation, and the Fractional Fourier Transform,” J. Opt. Soc. Am. 10,pp. 2181-2186, 1993.
[7] S. C. Pei and J. J. Ding, “Relations between Gabor Transform and Fractional Fourier Transforms and Their Applications for Signal Processing,” IEEE Trans. on Signal Processing, vol. 55, no. 10, pp. 4839-4850, Oct. 2007.
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References [8] Haldun M. Ozaktas, Zeev Zalevsky, M. Alper Kutay, The fractional Fourier
transform with applications in optics and signal processing, John Wiley & Sons, 2001.
[9] M. A. Kutay, H. M. Ozaktas, O. Arikan, and L. Onural, “Optimal Filter in Fractional Fourier Domains,” IEEE Trans. Signal Processing, vol. 45, no. 5, pp. 1129-1143, May 1997.
[10] J. J. Ding, “Research of Fractional Fourier Transform and Linear Canonical Transform,” Doctoral Dissertation, National Taiwan University, 2001.
[11] C. J. Lien, “Fractional Fourier transform and its applications,” National Taiwan University, June, 1999.
[12] Alan V. Oppenheim, Ronald W. Schafer and John R. Buck, Discrete-Time Signal Processing, 2nd Edition, Prentice Hall, 1999.
[13] R. N. Bracewell, The Fourier Transform and Its Applications, 3rd ed., Boston, McGraw Hill, 2000.
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Chenquieh!
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