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On Signal Reconstruction from Fourier Magnitude. Gil Michael Department of Electrical Engineering Technion - Israel Institute of Technology Haifa 32000, ISRAEL [email protected] Advisor: Dr. Moshe Porat. Lecture Outline. Introduction to Fourier Magnitude Reconstruction - PowerPoint PPT Presentation
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On Signal On Signal Reconstruction from Reconstruction from Fourier MagnitudeFourier Magnitude
Gil MichaelDepartment of Electrical Engineering
Technion - Israel Institute of Technology
Haifa 32000, [email protected]
Advisor: Dr. Moshe Porat
02/01/20012
Lecture OutlineLecture Outline1. Introduction to Fourier Magnitude
Reconstruction
2. Fourier Magnitude Applications
3. Reconstruction by Decimation-in-Time FFT
4. Reconstruction from Local Fourier Magnitudes
5. The Intermediate Fourier-Domain Algorithm (IFD)
6. Summary and Discussion
02/01/20013
IntroductionIntroduction
• Reconstruction of signals from their Fourier transform properties is a useful task in science and engineering.
• Fourier transform properties consist of phase and magnitude information:
1
0
1
0
1
0
/21
0
],[],[]},[{
:2
][][][]}[{
:1
N
n
N
m
knN
lmN
nkN
N
n
NknjN
n
WWnmxlkXnmxDFT
D
WnxenxkXnxDFT
D
02/01/20014
Introduction - Cont’d (2)Introduction - Cont’d (2)
•Discrete Fourier Transform (DFT) notation
1-D: ])[exp(|][|]}[{][ kjkXnxDFTkX
2-D:]),[exp(|],[|]},[{],[ lkjlkXnmxDFTlkX
02/01/20015
Reconstruction from Reconstruction from Fourier Magnitude or Fourier Magnitude or PhasePhase
DFT
Magnitude
PhaseID
FT
IDFT
Original
02/01/20016
Introduction - Cont’d (3)Introduction - Cont’d (3)
The Fourier phase contains higher intelligibility than Fourier magnitude
BUT
Fourier phase is more difficult to obtain
02/01/20017
Applications of Applications of Reconstruction from Reconstruction from Fourier MagnitudeFourier Magnitude
Detector
FourierTransformer
S
Fixed Mirror
Movable MirrorE i(t)
E(t) E(t+ )
L2L
1
•Spectroscopy
After: A.Yariv,Optical Electronics
02/01/20018
Applications of Applications of Reconstruction from Reconstruction from Fourier MagnitudeFourier Magnitude•Diffraction Pattern
02/01/20019
Applications of Applications of Reconstruction from Reconstruction from Fourier Magnitude-Cont’dFourier Magnitude-Cont’d•Crystallography
2
S iS r
O
P
A
B
(AO-PB)= r r*r*= S r-S i
=ScatteringAtoms
After:Ramachandran & Srinivasan, Fourier Methods in Crystallography
r
rr*i dverr*F 2)(}{
02/01/200110
Reconstruction from Reconstruction from Fourier Magnitude - Fourier Magnitude - LimitationsLimitationsGlobal Fourier magnitude is, in general, insufficient information for reconstruction
Additional data is required:
Spatial data, finite support, FT Sign- information, localized Fourier magnitude
02/01/200111
Reconstruction by Decimation-in-Time FFT
• From Nawab et al. (1983) we know, that a sequence x(n), which is zero outside 0
n N-1, is uniquely specified by its spectral magnitude and at least N/2 samples of x(n).
• The proof relies on the relationship between the Fourier magnitude and the auto-correlation function.
• The proposed algorithm is a closed-form solution to a similar problem:
Algorithm I
02/01/200112
Reconstruction by Decimation-in-Time FFT (Cont’d)
• Given the Fourier magnitude of a real signal x(n), which is zero outside 0 n
N-1, and the even (odd) samples, find a closed-form solution to the odd (even) unknown samples.
• Use the Decimation-in-Time FFT algorithm
* We will assume N is a power of 2, such that N=2 L for some
integer L>0
02/01/200113
Decimation-in-Time FFTDecimation-in-Time FFT
After: Cooley & Tukey, 1965
x[0]x[2]x[4]x[6]
x[1]x[3]x[5]x[7]
G[0]G[1]G[2]G[3]
N/2 PointFFT
N/2 PointFFT
H[0]H[1]H[2]H[3]
X[0]X[1]X[2]X[3]
X[4]X[5]X[6]X[7]
WN0
WN1
WN2
WN3
WN0
WN1
WN2
WN3
02/01/200114
Decimation-in-Time FFT Decimation-in-Time FFT (Cont’d)(Cont’d)
][][][ kHWkGkX kN
Hk
Gk jk
Nj ekHWekG ][][
•The problem of finding xodd [n] is equivalent to retrieving the N/2-point DFT values of xodd [n]:
120 Nk
(1)
02/01/200115
Decimation-in-Time FFT - Decimation-in-Time FFT - DetailDetail
][][][][...
][][][][][ 222
kHWkGkHWkG
kHkGkXkXkX
kN
kN
12,,2,1,0
][][][][...
][][]2/[ 222
Nk
kHWkGkHWkG
kHkGNkX
kN
kN
(a)
(b)
We may write the squared magnitude as:
02/01/200116
222
2 ][][2]2
[][ kHkGN
kXkX
And we obtain the absolute value of H[k] from:
For the retrieval of the phase K we
rewrite X[k] in the polar form:
(c)
Decimation-in-Time FFT - Decimation-in-Time FFT - DetailDetail
02/01/200117
120][][][ NkekHWekGkNXHk
Gk jk
Nj
Hk
Gk jk
Nj ekHWekGkX ][][][
)2
cos(][)cos(][)cos(][Nk
kHkGkX Hk
Gkk
)2
sin(][)sin(][)sin(][Nk
kHjkGjkXj Hk
Gkk
Summing and subtracting (d) and applying DFT properties of real signals,
(e)
(d)
Decimation-in-Time FFT - Decimation-in-Time FFT - DetailDetail
02/01/200118
)2
(cos][][2][][][ 222
Nk
kHkGkHkGkX Gk
Hk
Adding the square products of (e),
1202
][][2
][][][cos
222
NkNk
kHkG
kHkGkXa
Gk
Hk
And the resulting phase,
(f)
Decimation-in-Time FFT - Decimation-in-Time FFT - DetailDetail
02/01/200119
Decimation-in-Time FFT Decimation-in-Time FFT (Cont’d)(Cont’d)
120
2][][2
][][][cos
222
Nk
Nk
kHkG
kHkGkXa G
kHk
22
22 ][]2
[][21
][ kGN
kXkXkH
(2)
(3)
02/01/200120
Decimation-in-Time FFT Decimation-in-Time FFT (Cont’d)(Cont’d)
N
lk
kHjkHN
lH
kl
N
k kl
klevenevenodd
)(
,)cos()sin(
][][][
212
11 1
0
• Ambiguity in the phase of H[k] is resolved by using the interpolate by 2 formula:
(4)
02/01/200121
Decimation-in-Time FFT: Decimation-in-Time FFT: Extension to 2-D signals Extension to 2-D signals (Images)(Images)• An image may be fully reconstructed
using the Decimation-in-Time FFT approach, with some constraints imposed on it.
• The even rows (columns) must be symmetric i.e., in each even row (column), x(m,n)=x(N-m,n).
• The 2-D Fourier magnitude of x(m,n) and the 1-D Fourier magnitude of the even rows (columns) are known.
02/01/200122
Decimation-in-Time FFT: Decimation-in-Time FFT: The Intermediate Fourier The Intermediate Fourier DomainDomain•The 2-D DFT may be implemented by an FFT on the image’s rows (columns) and then on the resultant columns (rows).
•The intermediate Fourier domain (IFD) is defined as the resultant matrix obtained after the first row (column) FFT.
)]}n,m(x[DFT{DFT)}n,m(x{DFT row.col
02/01/200123
Decimation-in-Time FFT: Decimation-in-Time FFT: Extension to 2-D signals Extension to 2-D signals (Cont’d)(Cont’d)• Since the rows (columns) are
symmetric, their (1-D) Fourier transforms are real.
In addition:
• The columns (rows) of the image’s Fourier- transform are the (1-D) Fourier transforms of the IFD columns (rows).
02/01/200124
Decimation-in-Time FFT : Decimation-in-Time FFT : Intermediate Fourier Intermediate Fourier Domain ExampleDomain Example
DFT Performed On Columns
DFT
Columns
Rows
02/01/200125
Decimation-in-Time FFT: Decimation-in-Time FFT: Extension to 2-D signals Extension to 2-D signals (Cont’d)(Cont’d)• Using the Decimation-in-Time FFT
algorithm, reconstruct the image from:
1) The calculated even (odd) samples of the IFD columns
2) Fourier transform magnitude of the image
02/01/200126
Reconstruction from Local Reconstruction from Local Fourier MagnitudesFourier Magnitudes
• Recently, it was found that an image is represented by its Fourier magnitude and a quarter of its spatial samples (Shapiro & Porat, 1998)
• However, spatial samples may not always be available.
• In the following algorithm, image reconstruction from local Fourier magnitudes requires only a single spatial sample.
Algorithm II
02/01/200127
Reconstruction from Local Reconstruction from Local Fourier Magnitudes - Fourier Magnitudes - Cont’dCont’d
•Local Fourier magnitudes are the absolute values of Fourier transforms taken over specific sections of the image.
•Two methods of reconstructing an image from local Fourier magnitudes and a single spatial sample are presented here.
•In addition it is shown, that reconstruction quality is relatively unaffected by spatial data errors.
02/01/200128
Reconstruction from Local Reconstruction from Local Fourier Magnitudes - Fourier Magnitudes - Cont’dCont’d
•The first algorithm is based on successively reconstructing larger and larger image blocks, with each newly reconstructed block being the spatial samples required for the next iteration.
•The second algorithm, iteratively reconstructs equal-sized blocks that overlap by ¼ of their samples.
02/01/200129
Local Fourier magnitudes Local Fourier magnitudes – Successive Block-Size – Successive Block-Size Reconstruction AlgorithmReconstruction Algorithm
x(0,0)
X3
X4
X1
X2
02/01/200130
Local Fourier magnitudes Reconstruction Local Fourier magnitudes Reconstruction Algorithm– Successively larger Image Blocks Algorithm– Successively larger Image Blocks methodmethod
Known Spatial Block
02/01/200131
Local Fourier magnitudes Reconstruction Local Fourier magnitudes Reconstruction Algorithm– Overlapping Equal-Sized Blocks Algorithm– Overlapping Equal-Sized Blocks method (32x32 pixels)method (32x32 pixels)
Known Spatial Block
02/01/200132
Reconstruction from Local Reconstruction from Local Fourier Magnitudes and Fourier Magnitudes and an Arbitrary Spatial an Arbitrary Spatial SampleSample
Original Reconstructed
Note: inconsistency
02/01/200137
The Intermediate Fourier- The Intermediate Fourier- Domain Reconstruction Domain Reconstruction AlgorithmAlgorithm• Gerchberg-Saxton algorithm:
iteratively imposes spatial constraints, known samples and Fourier magnitude in the spatial and Fourier domains
• As shown previously, the 2-D DFT may be implemented using the Intermediate Fourier Domain (IFD).
Algorithm III
02/01/200138
The Intermediate Fourier-The Intermediate Fourier-Domain Reconstruction Domain Reconstruction Algorithm - Cont’dAlgorithm - Cont’d• A typical reconstruction scheme would
require a 2N x 2N DFT for an N x N image, essentially zero-padding the additional spatial samples:
ImageN x N
0
00
0
2N
2N
02/01/200139
The Intermediate Fourier-The Intermediate Fourier-Domain Reconstruction Domain Reconstruction Algorithm - Cont’dAlgorithm - Cont’d
• The IFD, preserves some of the spatial constraints:
1. Finite support.
2. Nulling of the columns (rows) of the result.
02/01/200140
The Intermediate Fourier-The Intermediate Fourier-Domain Reconstruction Domain Reconstruction Algorithm - Cont’dAlgorithm - Cont’d
• The proposed algorithm utilizes these properties to modify the Gerchberg-Saxton approach.
• It uses 1-D FFT’s only, and imposes twice as many magnitude constraints in each iteration, resulting in faster convergence to the desired image.
02/01/200141
Intermediate Intermediate Fourier-Fourier-Domain Domain Algorithm: Algorithm: Block Block DiagramDiagram
x_temp: K known rows,K known columnsX_row=DFT of x_temp rows
X_col=DFT of x_temp columnsX_temp=2-D DFT of x_temp
Impose Fourier magnitude onX_temp
Impose X_col on X_int_rNull columns N to 2N-1
X_temp = DFT{X_int_r} on rows
Impose Fourier magnitude onX_temp
X_int_r = IDFT{X_temp}on rows
X_int_c = IDFT{X_temp} on columns
Impose X_row on X_int_cNull rows N to 2N-1
X_temp = DFT{X_int_r} on columns
02/01/200142
Intermediate Fourier-Intermediate Fourier-Domain Algorithm: Domain Algorithm: Iterative SimulationIterative Simulation
Iteration: 1234567891
0
25% known spatial samples
02/01/200143
Intermediate Fourier-Intermediate Fourier-Domain Algorithm: Domain Algorithm: ComparisonComparison
After 10 Iterations
02/01/200144
Intermediate Fourier-Intermediate Fourier-Domain Algorithm: Domain Algorithm: Simulation Results Simulation Results After 100 IterationsAfter 100 Iterations
18dB
02/01/200145
SummarySummary• Three new approaches to signal
reconstruction from Fourier magnitude were presented.
I) Decimation-in-Time FFT Algorithm:
Closed-form solution: No iterations and convergence uncertainty.
Lower computational complexity compared to the auto-correlation approach.
Efficient implementation (FFT).
2-D Extension requires only Fourier information.
02/01/200146
Summary - Cont’dSummary - Cont’d
II) Localized Fourier Transform Magnitude Algorithm:
Magnitude information and a single spatial sample fully reconstruct the image.
Two methods may be applied: constant block or successively larger block reconstruction.
This algorithm may prove useful in phase retrieval problems, where spatial information is unavailable (in contrast to Nawab et al.).
02/01/200147
Summary - Cont’dSummary - Cont’d
III) Intermediate Fourier Domain Algorithm
A modified Gerchberg-Saxton algorithm utilizes the separability property of the 2-D DFT.
Imposes the Fourier magnitude constraints at twice the rate of the conventional approach, resulting in faster convergence rates.
02/01/200148
Thank You For Your Attention !Thank You For Your Attention !