distributed feature-specific imaging
DESCRIPTION
Distributed Feature-Specific Imaging. Jun Ke 1 , Premchandra Shankar 1 , and Mark A. Neifeld 1,2. 1 Department of Electrical and Computer Engineering, 2 College of Optical Sciences University of Arizona. Computational Optical Sensing and Imaging (COSI) 2007. Outline. Motivation - PowerPoint PPT PresentationTRANSCRIPT
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Distributed Feature-Specific Imaging
Jun Ke1, Premchandra Shankar1, and Mark A. Neifeld1,2
Computational Optical Sensing and Imaging (COSI) 2007
1Department of Electrical and Computer Engineering, 2College of Optical Sciences
University of Arizona
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Outline
Motivation
Distributed feature-specific imaging system
System performance – reconstruction error & lifetime
Experimental result
Conclusion
COSI2007
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Conventional imaging :
Feature-specific imaging (FSI) :
Background
COSI2007
measured image
x+n
noise n
objectx
conventional imager
collected irradiance
post processing
reconstructionx̂
objectx
estimated featureFx+n
noise n
collected irradiance
feature specific imager
post processing
reconstructionx̂
Projections: PCA, DCT and Hadamard, etc.
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Distributed feature-specific imaging (DFSI):
k > 1 → DFSI → mk = (FkGk-1) Gkx + nk = Fkx + n
Motivation
COSI2007
Distributed conventional imaging (DCI):
n
Imager K
Imager 1
n
Object: x
n
Imager 2
Base station
# of measurement
←Large Small→
Complexity
←High Low→
Redundancy
←High Low→
Size/Weight/Power
←High Low→
Bandwidth
←High Low→
Lifetime
←Short Long→
Characteristics
Object: x
Base station
n
Imager 2
Imager 1
n
n
Imager K
k = 1 → FSI → m = F x + n
Gk ~ geometric transform for the kth imager.
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Parallel FS imager:
Imaging -Optics
Fixed Mask
L – Detector Array
mi = fi x + n, i=1, …, L
n
Noisy Measurements
Object: x
Feature-specific Imaging Architecture
Noise variance is proportional to σ02/T0.
Sequential FS imager:
Imaging Optics
Light Collection Optics
Programmable Mask
Single Photo-Detector
n
Noisy Measurement
Object: x
mi = fi x + n i=1, …, L
COSI2007
Noise variance is proportional to Lσ02/T0 .
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System Performance – Reconstruction Error
COSI2007
Object examples (32x32):
T -1x x DW = R F(FR F +R )
{ }E TxR x x
Wiener operator is used for reconstruction:
where,: noise auto-correlation matrixDR
x̂ = Wy Reconstructed object:
2ˆ{|| || }/E N x - x RMSE:
m = Fx n Feature measurements: where, : 1 : : 1N M N M x F n
For k imager DFSI, features are measured by each imager./M k
is the total # of featuresM
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System Performance – Reconstruction Error
There is a minimum RMSE for each curve.
Parallel FSI is better than sequential FSI in term of RMSE.
PCA reaches minimum using small number of features.
PCA has the best performance when # of features is small.
Hadamard has the best performance when # of features is large.
COSI2007
M = total # of featuresM = total # of features
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high noise
moderate noise
low noise
As k increases,
System collected photons increases
# of features per imager decrease
Photons per feature increases
RMSE reduces
Using more imagers will increase fidelity
System Performance – Reconstruction Error
COSI2007
When noise is high, PCA and Hadamard projections have similar performances
When noise is moderate or low, Hadamard produces the smallest minimum RMSE
Generally, Hadamard projection is the best candidate for noisy environment.
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Lifetime
~ total # of data transmitted before energy runs out / # of data in each transmission
Normalized lifetime in DFSI:
~ Lifetime of DFSI / Lifetime of conventional imaging system = N/(M/k)
Compression has not been considered in both systems
System Performance – Lifetime
COSI2007
n
Imager K
Imager 1
n
Object: x
n
Imager 2
Base station
NN
M/k
M/k
M/k
M/k
M/k
M/k
DFSI:
NN
N
N
NDCI:
Object: x
Base station
n
Imager 2
Imager 1
n
n
Imager K
N
N
N
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Lifetime reduces as RMSE performance requirement is higher.
Lifetime is enlarged as more imagers are used.
With non-strict RMSE requirement, DFSI using PCA has the longest lifetime.
With strict requirement of RMSE, DFSI using Hadamard is the best option.
Generally, DFSI with PCA present the best performance in term of lifetime.
Normalized lifetime with different projections and different number of imagers k:
System Performance – Lifetime
COSI2007
k
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Experiment
There is a minimum RMSE for each curve
RMSE reduces as K increases.
COSI2007
σ0 = 10-3 1200 Hadamard features
original object k = 1,rmse=0.44 k = 2,rmse=0.18
k = 3,rmse=0.12 k = 4,rmse=0.10 k = 5,rmse=0.09
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Conclusion
DFSI preserves FSI properties.
DFSI has better performance compared with FSI
Hadamard is the best projection in term of reconstruction error.
PCA is the best projection in term of system lifetime.
COSI2007
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Block-wise data testing
Random projection has the biggest RMSE
PCA achieves minimum RMSE quick
Hadamard performs better with more features
Experiment - result
COSI2007
σ0 = 10-4, Hadamard 600 features