MIT CSAIL - Computational Imagingpeople.csail.mit.edu/bkph/talkfiles/Computational...3×3 node grid...
Transcript of MIT CSAIL - Computational Imagingpeople.csail.mit.edu/bkph/talkfiles/Computational...3×3 node grid...
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COMPUTATIONAL IMAGING
Berthold K.P. Horn
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What is Computational Imaging?
• Computation inherent in image formation
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What is Computational Imaging?
• Computation inherent in image formation
(1) Computing is getting faster and cheaper
—precision physical apparatus is not
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What is Computational Imaging?
• Computation inherent in image formation
(1) Computing is getting faster and cheaper
—precision physical apparatus is not
(2) Can’t refract or reflect some radiation
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What is Computational Imaging?
• Computation inherent in image formation
(1) Computing is getting faster and cheaper
—precision physical apparatus is not
(2) Can’t refract or reflect some radiation
(3) Detection is at times inherently coded
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Computational Imaging System
Berthold K.P. HornLine
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Examples of Computational Imaging:
(1) Synthetic Aperture Imaging
(2) Coded Aperture Imaging
(3) Exact Cone Beam Reconstruction
(4) Diaphanography—DiffuseTomography
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(1) SYNTHETIC APERTURE IMAGING
Traditional approach:
• Coupling of resolution, DOF, FOV to NA• Precision imaging — “flat” illumination
with: Michael Mermelstein, Jekwan Ryu,Stanley Hong, and Dennis Freeman
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Objective Lens Parameter Coupling
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Synthetic Aperture Imaging
Traditional approach:
• Coupling of resolution, DOF, FOV to NA• Precision imaging — “flat” illuminationNew approach:
• Precision illumination — Simple imaging• Multiple images — Textured illumination
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Synthetic Aperture Imaging
• Precision illumination — Simple imaging• Multiple images — Textured illumination
• Image detail in response to textures• Non-uniform samples in FT space
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SAM M6
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Creating Interference Pattern
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Reflective Optics M6
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Creating Interference Pattern
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Fourier Transform of Texture Pattern
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Interference Pattern Texture
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Synthetic Aperture Microscopy
• Interference of many Coherent Beams• Amplitude and Phase Control of Beams
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Amplitude and Phase Control
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Synthetic Aperture Microscopy
• Interference of many Coherent Beams• Amplitude and Phase Control of Beams
• On the fly calibration• Non-uniform inverse FT Least Squares
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Wavenumber Calibration using FT
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Hough Transform Calibration
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Least Squares Match in FT
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Polystyrene Micro Beads (1µm)
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(2) CODED APERTURE IMAGING
• Can’t refract or reflect gamma rays• Pinhole — tradeoff resolution and SNR
with: Richard Lanza, Roberto Accorsi,Klaus Ziock, and Lorenzo Fabris.
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Coded Aperture Imaging
• Can’t refract or reflect gamma rays• Pinhole — tradeoff resolution and SNR
• Multiple pinholes• Complex masks can “cast shadows”
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Coded Aperture Principle
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Decoding Method Rationale
Berthold K.P. HornStamp
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Coded Aperture Imaging
• Can’t refract or reflect gamma rays• Pinhole — tradeoff resolution and SNR• Complex masks can “cast shadows”
• Decoding by Correlation• Special Masks with Flat Power Spectrum
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Mask Design — Inverse Systems
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Masks — XRT Coarse
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Mask Design — 1D
Definition: q is a quadratic residue (mod p)if ∃n s.t. n2 ≡ q(mod p)Legendre symbol(
ap
)={
1 if a is quadratic residue−1 otherwise
Correlation with zero shift (p − 1)/2Correlation with non-zero shift (p − 1)/4
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Mask Design
• Auto Correlation
a(i) = (p − 1)4
(1+ δ(i))
• Power Spectrum
A(j) = (p − 1)4
(δ(j)+ 1)
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Coded Aperture Extensions
• Imaging Nearby Objects• Mask / Countermask Combination* Dynamic Reconstruction
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Coded Aperture Application
• Detection of Fissile Material• Large Area Detector Myth• Signal and Background Amplified
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Large Area Alone Doesn’t Help
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Imaging and Large Area Do!
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Coded Aperture Example
• Imaging — 1/R instead of 1/R2
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Coded Aperture Detector Array
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Computational Imaging System
Berthold K.P. HornLine
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Dynamic Reconstruction
Three weak, distant radioactive sources
Reconstruction Animation
http://csail.mit.edu/~bkph/images/Back-402.html
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Coded Aperture Applications
• Detection of Fissile Material• Imaging — 1/R instead of 1/R2
• Increasing Gamma Camera Resolution• Replacing Rats with Mice
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(4) EXACT CONE BEAM ALGORITHM
• Faster Scanning—Fewer Motion Artifacts• Lower Exposure—Uniform Resolution
with: Xiaochun Yang
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Exact Cone Beam Reconstruction
• Faster Scanning—Fewer Motion Artifacts• Lower Exposure—Uniform Resolution
• Parallel Beam → Fan Beam• Planar Fan → Cone Beam
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Parallel Beam to Fan Beam
Coordinate Transform in 2D Radon Space
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Cone Beam Geometry — 3D
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Radon’s Formula
• In 2D: ~ derivatives of line integrals• In 3D: derivatives of plane integrals• Can’t get plane integrals from projections∫ (∫
f(r , θ)dr)dθ
∫ ∫1rf(x,y)dx dy
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Radon’s Formula in 3D
f(x) = − 18π2
∫S2∂2R f(l,β)
∂l2
∣∣∣∣∣l=x·β
dβ
where
R f(l,β) =∫f(x) δ(x · β− l)dV
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Grangeat’s Trick
∂∂z
∫ ∫f(x,y, z)dx dy =
∂∂θ
∫ ∫f(r ,φ,θ)dr dφ
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Exact Cone Beam Reconstruction
• Data Sufficiency Condition• Good “Orbit” for Radiation Source
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Radon Space — 2D
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Circular Orbit is Inadequate (3D)
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Data Insufficiency
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Good Source Orbit
http://csail.mit.edu/~bkph/movies/ball_seams_45.pdf
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Exact Cone Beam Reconstruction
• Data Sufficiency Condition• Good “Orbit” for Radiation Source
• Practical Issue: Spiral CT Scanners• Practical Issue: “Long Body” Problem
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(3) DIAPHANOGRAPHY
(Diffuse Optical Imaging)
• Highly Scattering — Low Absorption• Many Sources — Many Detectors
with: Xiaochun Yang, Richard Lanza, David Boas and Anna Custo.
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2
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Diaphanography
• Randomization of Direction
• Scalar Flux Density
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mm
mm
Head coronal slice
60 80 100 120 140 160
30
40
50
60
70
80
90
100
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scalp
white
gray
CSF
skull
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(a)
(b)
10 15 20 25 30 35 40−8
−7.5
−7
−6.5
−6
−5.5
−5
−4.5
−4
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source−detector separation [mm]
Log 1
0 F
luen
ce
Total fluence [CW]0.010.11.0
15 20 25 30 35 40
−0.25
−0.2
−0.15
−0.1
−0.05
0
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source−detector separation [mm]
(MC
− M
Co)
/ M
Co
Deviation of fluence
0.0010.010.11.0
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(b)
15 20 25 30 35 400
0.1
0.2
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0.6
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source−detector separation [mm]
Nor
mal
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PP
F
scalp−skull
brain
1.00.10.01
15 20 25 30 35 40−0.01
−0.005
0
0.005
0.01
0.015
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detector separation [mm]
(PP
F −
PP
Fo)
/PP
Fo
Deviation of Sensitivity to Scalp−Skull
0.001 0.010.11.0
15 20 25 30 35 40−0.5
−0.4
−0.3
−0.2
−0.1
0
0.1
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detector separation [mm]
(PP
F −
PP
Fo)
/PP
Fo
Deviation of Sensitivity to Brain
0.001
0.01
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1.0
(c)
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(a)
(b)
−20
−15
−10Total fluence [TD]
−20
−15
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Log 1
0TP
SF
0.5 1 1.5 2 2.5 3−20
−18
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time steps [ns]
0.01
0.1
1.020 mm
30 mm
40 mm
0
0.5
1Deviation of fluence
0
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(MC
− M
Co)
/ M
Co
0.5 1 1.5 2 2.5 3−0.5
0
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time steps [ns]
0.001 0.01 0.1 1.0
20 mm
30 mm
40 mm
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Diaphanography
• Approximation: Diffusion Equation
∆v(x,y)+ ρ(x,y)c(x,y) = 0v(x,y) flux densityρ(x,y) scattering coefficientc(x,y) absorption coefficient
• Forward: given c(x,y) find v(x,y)
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3 × 3 node grid example
Here we have three input nodes (1, 2, 3), three output nodes (7, 8, 9), and three interior nodes (4, 5, 6).In three experiments we apply currents to each of the input nodes in turn, each time reading out all of theoutput nodes, yielding a total of nine measurements. We try and recover the nine leakage conductances toground from each of the nine nodes.
It is natural to partition the conductance matrix as follows given that I4, I5, I6, I7, I8, and I9 are alwayszero, and that we do not measure V1, V2, V3, V4, V5, and V6.
⎛⎜⎜⎜⎜⎜⎜⎝
G11... G12
... G13· · · · · · · · · · ·G21
... G22... G23
· · · · · · · · · · ·G31
... G32... G33
⎞⎟⎟⎟⎟⎟⎟⎠
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
V1V2V3· · ·V4V5V6· · ·V7V8V9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
=
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
I1I2I3· · ·I4I5I6· · ·I7I8I9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
In detail
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
g′1 −g12 −g13... −g14 0 0
... 0 0 0
−g12 g′2 −g23... 0 −g25 0
... 0 0 0
−g13 −g23 g′3... 0 0 g36
... 0 0 0· · · · · · · · · · · · · · · · · · · · · · · · · · · · ·
−g14 −0 0... g′4 −g45 −g46
... −g47 0 00 −g25 0
... −g45 g′5 −g56... 0 −g58 0
0 0 −g36... −g46 −g56 g′6
... 0 −0 −g69· · · · · · · · · · · · · · · · · · · · · · · · · · · · ·0 0 0
... −g47 0 0... g′7 −g78 −g79
0 0 0... 0 −g58 0
... −g78 g′8 −g890 0 0
... 0 0 −g69... −g79 −g89 g′9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
V1V2V3· · ·V4V5V6· · ·V7V8V9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
=
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
I1I2I3· · ·I4I5I6· · ·I7I8I9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
where g′1 = (g1 + g12 + g13 + g14), g′2 = (g2 + g12 + g23 + g25), g
′3 = (g3 + g13 + g23 + g36), g
′4 = (g4 + g14 +
g45 + g46 + g47), g′5 = (g5 + g25 + g45 + g56 + g47), g′6 = (g6 + g36 + g46 + g56 + g69), g
′7 = (g7 + g47 + g78 + g79),
g′8 = (g8 + g58 + g78 + g89), and g′9 = (g9 + g69 + g79 + g89).
We note that G13 and G31 are all zeros, and G12 = G21, and G23 = G32 are diagonal. Also, thesub-matrices appearing on the diagonal are of Toeplitz form. Tpeolitz matrices can be inverted in order N2
(instead of order N3).
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3 × 3 node grid example
Here we have three input nodes (1, 2, 3), three output nodes (7, 8, 9), and three interior nodes (4, 5, 6).In three experiments we apply currents to each of the input nodes in turn, each time reading out all of theoutput nodes, yielding a total of nine measurements. We try and recover the nine leakage conductances toground from each of the nine nodes.
It is natural to partition the conductance matrix as follows given that I4, I5, I6, I7, I8, and I9 are alwayszero, and that we do not measure V1, V2, V3, V4, V5, and V6.
⎛⎜⎜⎜⎜⎜⎜⎝
G11... G12
... G13· · · · · · · · · · ·G21
... G22... G23
· · · · · · · · · · ·G31
... G32... G33
⎞⎟⎟⎟⎟⎟⎟⎠
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
V1V2V3· · ·V4V5V6· · ·V7V8V9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
=
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
I1I2I3· · ·I4I5I6· · ·I7I8I9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
In detail
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
g′1 −g12 −g13... −g14 0 0
... 0 0 0
−g12 g′2 −g23... 0 −g25 0
... 0 0 0
−g13 −g23 g′3... 0 0 g36
... 0 0 0· · · · · · · · · · · · · · · · · · · · · · · · · · · · ·
−g14 −0 0... g′4 −g45 −g46
... −g47 0 00 −g25 0
... −g45 g′5 −g56... 0 −g58 0
0 0 −g36... −g46 −g56 g′6
... 0 −0 −g69· · · · · · · · · · · · · · · · · · · · · · · · · · · · ·0 0 0
... −g47 0 0... g′7 −g78 −g79
0 0 0... 0 −g58 0
... −g78 g′8 −g890 0 0
... 0 0 −g69... −g79 −g89 g′9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
V1V2V3· · ·V4V5V6· · ·V7V8V9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
=
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
I1I2I3· · ·I4I5I6· · ·I7I8I9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
where g′1 = (g1 + g12 + g13 + g14), g′2 = (g2 + g12 + g23 + g25), g
′3 = (g3 + g13 + g23 + g36), g
′4 = (g4 + g14 +
g45 + g46 + g47), g′5 = (g5 + g25 + g45 + g56 + g47), g′6 = (g6 + g36 + g46 + g56 + g69), g
′7 = (g7 + g47 + g78 + g79),
g′8 = (g8 + g58 + g78 + g89), and g′9 = (g9 + g69 + g79 + g89).
We note that G13 and G31 are all zeros, and G12 = G21, and G23 = G32 are diagonal. Also, thesub-matrices appearing on the diagonal are of Toeplitz form. Tpeolitz matrices can be inverted in order N2
(instead of order N3).
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We can write the inverse as follows:
⎛⎜⎜⎜⎜⎜⎜⎝
C11... C12
... C13· · · · · · · · · · ·C21
... C22... C23
· · · · · · · · · · ·C31
... C32... C33
⎞⎟⎟⎟⎟⎟⎟⎠
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
I1I2I3· · ·I4I5I6· · ·I7I8I9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
=
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
V1V2V3· · ·V4V5V6· · ·V7V8V9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
We can use the formula for the inverse of matrix partitioned into four parts twice on this matrix partitionedinto nine parts. But it may be a bit much to expect to easily obtain explicit formulae the way we did forthe 2 × 2 case. . .
Note that we are only really interested in the bottom left corner (C31) of the inverse, given that I4, I5,I6, I7, I8, and I9 are always zero, and that we do not measure V1, V2, V3, V4, V5, and V6. Each experimentyields three measurements and thus three equations of the form
C31
⎛⎝ I1I2
I3
⎞⎠ =
⎛⎝ V7V8
V9
⎞⎠ .
By performing three experiments we can find all nine elements of the matrix C31. Each of these is apolynomial in the unknown leakage conductances g1, g2, g3, g4, g5, g6, g7, g8, and g9 (or rather, we cancross-multiply to obtain nine such polynomials).
The part of the inverse of this conductance matrix that we need is the lower left corner, C31. Using thedecomposition rule for partitioned 2 × 2 matrices twice, we get
C31 = G−133 G32(G22 − G23G−133 G21)−1G21(G11 − G12(G22 − G23G−133 G21)−1G21
)−1
Note that the term (G22 −G23G−133 G21)−1G21 appears twice. This can be exploited to save on computation.
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FORWARDSOLUTION
UPDATE RULE -"ASSIGNING BLAME"
I’(r1,r2)I(r1,r2)
c(k)(r)
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Diaphanography
• “Invert” Diffusion Equation
• Regions of Influence
.
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COMPUTATIONAL IMAGING
(1) Synthetic Aperture Imaging
(2) Coded Aperture Imaging
(3) Exact Cone Beam Reconstruction
(4) Diaphanography—DiffuseTomography
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COMPUTATIONAL IMAGING
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Synthetic Aperture Lithography
• Create pattern — controlled interferenceExample: Two Dots
Example: Straight Line
• Destructive interference “safe zone”Example: Bessel Ring
.
http://www.csail.mit.edu/~bkph/images/Binary_Stars.gifhttp://csail.mit.edu/~bkph/images/Linear_Growth.gifhttp://csail.mit.edu/~bkph/images/Color_Ring.gifhttp://csail.mit.edu/~bkph/images/Binary_Stars.gifhttp://csail.mit.edu/~bkph/images/Linear_Growth.gifhttp://csail.mit.edu/~bkph/images/Color_Ring.gif
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SAM M4
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Amplitude and Phase Control
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Interference Pattern Texture
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Fourier Transform of Texture Pattern
Berthold K.P. HornRectangle
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Uneven Fourier Sampling
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Resolution Enhancement
• Reflective Optics IlluminationVaccum UV — Short Wavelength
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Resolution Enhancement
• Reflective Optics IlluminationVaccum UV — Short Wavelength
• Fluorescence ModeResolution Determined by Illumination
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Masks — Fresnel Camera
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Masks — Legri URA
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Masks — XRT Fine
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Masks — Hexagonal
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Maximizing SNR
minn∑i=1w2i subject to
n∑i=1wi = 1
yields wi = 1n
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Spatially Varying Background
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Coded Aperture Extensions
• Artifacts due to Finite Distance• Mask / Countermask Combination
• Multiple Detector Array Positions• “Synthetic Aperture” Radiography
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Dynamic Reconstruction
http://csail.mit.edu/~bkph/images/Coded_Backprojection.html
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Diaphanography
• Approximation: Diffusion Equation
• Leaky Resistive Sheet Analog (2D)
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(a)
(b)
(c)
1 2 30
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
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120mm
time steps [ns]
Nor
mal
ized
PP
F
1 2 3
30mm
1 2 3
40mm
scalp skull
brain
0.010.11.0
−0.05
0
0.05Deviation of Sensitivity to Scalp−Skull
−0.1
0
0.1
(PP
F −
PP
Fo)
/PP
Fo
0.5 1 1.5 2 2.5 3−0.2
0
0.2
time steps [ns]
0.001 0.01 0.1 1.0
20 mm
30 mm
40 mm
−1
0
10.5
−0.5
Deviation of Sensitivity to Brain
−1
0
1
−0.5
0.50.5
(PP
F −
PP
Fo)
/PP
Fo
0.5 1 1.5 2 2.5 3−1
0
1
0.5
−0.5−0.5
time steps [ns]
0.001 0.01 0.1 1.0
20 mm
30 mm
40 mm
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Sample Resistive Grid Inversion
Berthold K.P. Horn — 1996 February 14th
Here we consider about the simplest possible case of the two-d resistive sheet to get some insight into themore general problem.
(draw your own picture here :)There are four nodes, arranged in N = 2 rows and M = 2 columns. On the left are the two nodes used
for input ((1) and (2)) and on the right are the two output nodes ((3) and (4)). Four ‘horizontal’ resistorswith conductance g12, g13, g24 and g34 connect these four nodes. These resistors represent scattering, andare assumed to be of known value. There is also a ‘vertical’ leakage path from each of the four nodes toground — with conductances g1, g2, g3 and g4. These represent absorption, and are the unknowns.
We are to recover the values of the unknown leakage resistors. We perform two experiments: First weinject current at node (1) and measure the potentials on nodes (3) and (4). Call the ‘trans-impedance’ (ratioof output potential to injected current) observed this way R3,1 and R4,1. Then we inject instead current atnode (2) and again measure the potential on nodes (3) and (4). Call the ‘trans-impedance’ observed thisway R3,2 and R4,2.
If the grid was N ×M instead of 2×2 then we would have performed N experiments, each time injectingcurrent on one of the N input nodes and reading out the potential on each of the N output nodes. We thentry to recover the N ×M unknown leakage conductances to ground. Clearly there is not enough constraint ifM > N since there are then more unknowns than measurements. Conversely if M < N , we have redundantinformation and may want to use a least squares approach to obtain the best possible answer.
Here we deal with the simple case where M = N = 2. The node equations in this case are:
I1 = g1V1 + g13(V1 − V3) + g12(V1 − V2)I2 = g2V2 + g12(V2 − V1) + g24(V2 − V4)I3 = g3V3 + g13(V3 − V1) + g34(V3 − V4)I4 = g4V4 + g24(V4 − V2) + g34(V4 − V3)
or ⎛⎜⎜⎜⎜⎜⎜⎜⎝
(g1 + g13 + g12) −g12... −g13 0
−g12 (g2 + g12 + g24)... 0 −g24
· · · · · · · · · · · · ·−g13 0
... (g3 + g13 + g34) −g340 −g24
... −g34 (g4 + g24 + g34)
⎞⎟⎟⎟⎟⎟⎟⎟⎠
⎛⎜⎜⎜⎝
V1V2· · ·V3V4
⎞⎟⎟⎟⎠ =
⎛⎜⎜⎜⎝
I1I2· · ·I3I4
⎞⎟⎟⎟⎠
Note that all off diagonal elements are negative, and that the unknown leakage conductances all appear onthe diagonal. Also, the matrix becomes singular if all leakages conductances are set to zero, since then eachrow adds up to zero.
Making use of the partitioning indicated above, we can write
⎛⎜⎝ G11
... G12· · · · · · ·G21
... G22
⎞⎟⎠
⎛⎜⎜⎜⎝
V1V2· · ·V3V4
⎞⎟⎟⎟⎠ =
⎛⎜⎜⎜⎝
I1I2· · ·I3I4
⎞⎟⎟⎟⎠
This partitioning is convenient, since in the experiments we always have I3 = 0 and I4 = 0, and since V1 andV2 are not known.
If we invert this set of equations we get:
⎛⎜⎝ C11
... C12· · · · · · ·C21
... C22
⎞⎟⎠
⎛⎜⎜⎜⎝
I1I2· · ·I3I4
⎞⎟⎟⎟⎠ =
⎛⎜⎜⎜⎝
V1V2· · ·V3V4
⎞⎟⎟⎟⎠
1
-
whereC11 = (G11 − G12G−122 G21)−1C21 = −G−122 G21C11C22 = (G22 − G21G−111 G12)−1C12 = −G−111 G12C22
Upon multiplying out we obtain
C11
(I1I2
)=
(V1V2
)
where the term involving C12 drops out because I3 = 0 and I4 = 0. This equation is of no interest since wedon’t know V1 and V2. But we also obtain
C21
(I1I2
)=
(V3V4
)
where the term involving C22 drops out because I3 = 0 and I4 = 0. The four unkowns (g1, g2, g3, g4) occur inthe matrix C21, but we obviously can’t solve for them using a single set of measurements. We can however,combine two sets of measurements and obtain:
C21
(I1,1 I1,2I2,1 I2,2
)=
(V3,1 V3,2V4,1 V4,2
)
where typically we would choose I1,1 = 1, I2,1 = 0 for the first experiment and I1,2 = 0, I2,2 = 1 for thesecond. We then obtain
C21 =(
R3,1 R3,2R4,1 R4,2
)
where, provided I2,1 = 0 and I1,2 = 0, R3,1 = V3,1/I1,1, R4,1 = V4,1/I1,1, and R3,2 = V3,2/I1,2, R4,2 =V4,2/I1,2. So from image measurements we can recover the matrix C21, and we know that
C21 = −G−122 G21C11
orC21C
−111 = −G−122 G21
then, sinceC11 = (G11 − G12G−122 G21)−1
we obtainC21(G11 − G12G−122 G21) = −G−122 G21
We need to manipulate this some more to try and isolate the two matrices G11 and G22, which contain theunknowns g1, g2, g3, and g4. We see that
C21G11 = C21G12G−122 G21 − G−122 G21
orC21G11G
−121 G22 = C21G12 − I
or finallyG11G
−121 G22 = G12 − C−121
Here C−121 is obtained from experimental measurements, while G11 and G22 contain the unknown leakageconductances. In our simple example, G12 and G21 are diagonal.
2
-
Numerical Example
Suppose that the ‘horizontal’ resistors have conductance as follows: g13 = 1, g12 = 2, g24 = 1, and g34 = 2.Next assum that the leakage or ‘vertical’ resistors have conductance g1 = 1, g2 = 2, g3 = 2, and g4 = 1.Then the conductance matrix is ⎛
⎜⎜⎜⎜⎜⎜⎜⎝
4 −2 ... −1 0−2 5 ... 0 −1· · · · · · · · · · · · ·−1 0 ... 5 −20 −1 ... −2 4
⎞⎟⎟⎟⎟⎟⎟⎟⎠
and so
G−111 =116
(5 22 4
)and G−122 =
116
(4 22 5
)
and so
G12G−122 G21 = G
−122 =
116
(4 22 5
)
while
G21G−111 G12 = G
−111 =
116
(5 22 4
)
so
G11 − G12G−122 G21 =116
(60 −34
−34 75)
and
G22 − G21G−111 G12 =116
(75 −34
−34 60)
So in the inverse we have
C11 =1
209
(75 3434 60
)and C22 =
1209
(60 3434 75
)
and
C21 =1
209
(23 1620 23
)and C12 =
1209
(23 2016 23
)
Finally
G−1 = C =1
209
⎛⎜⎜⎜⎜⎜⎜⎝
75 34... 23 20
34 60... 16 23
· · · · · · · · · · · · ·23 16
... 60 34
20 23... 34 75
⎞⎟⎟⎟⎟⎟⎟⎠
In the first experiment we have I1 = 1 and the other node currents are zero so⎛⎜⎝
V1V2V3V4
⎞⎟⎠ = 1209
⎛⎜⎝
75342320
⎞⎟⎠ .
In the second experiment we have I2 = 1 and the other node currents are zero so⎛⎜⎝
V1V2V3V4
⎞⎟⎠ = 1209
⎛⎜⎝
34601623
⎞⎟⎠ .
3
-
Note that we can only measure V3 and V4 in each case. This is the end of the ‘forward’ problem (findingtrans-impedance given leakage conductances).
The ‘inverse’ task is to recover the unknown leakage conductances. Extracting the relevant parts fromthe above ‘experimental’ data we see that
C21 =1
209
(23 1620 23
).
so
C−121 =(
23 −16−20 23
).
so
G12 − C−121 =( −24 16
20 24
).
and
G11 =(
g1 + 3 −2−2 g2 + 3
)and G22 =
(g3 + 3 −2
−2 g4 + 3)
.
So
G11G−121 G22 =
(g1 + 3 −2
−2 g2 + 3) (
g3 + 3 −2−2 g4 + 3
).
So that we get the following equations in the unknown leakage conductances:
(g1 + 3)(g3 + 3) + 4 = 242(g1 + 3) + 2(g4 + 3) = 162(g3 + 3) + 2(g2 + 3) = 20(g2 + 3)(g4 + 3) + 4 = 24
orḡ1ḡ3 = 20
ḡ1 + ḡ4 = 8ḡ2 + ḡ3 = 10
ḡ2ḡ4 = 20
where ḡ1 = g1 +3, ḡ2 = g2 +3, ḡ3 = g3 +3, and ḡ4 = g4 +3. These equations have only one solution: ḡ1 = 4,ḡ2 = 5, ḡ3 = 5, and ḡ4 = 4, that is g1 = 1, g2 = 2, g3 = 2, and g4 = 1.
Summary
While this shows a solution method for a 2 × 2 grid, some of the points noted here also apply to the moregeneral case, although an explicit solution cannot be expected then. In general, the matrix would have to bepartitioned into a 3 × 3 arrangement corresponding to the fact that in addition to input nodes and outputnodes there are then also interior nodes.
4
-
We can write the inverse as follows:
⎛⎜⎜⎜⎜⎜⎜⎝
C11... C12
... C13· · · · · · · · · · ·C21
... C22... C23
· · · · · · · · · · ·C31
... C32... C33
⎞⎟⎟⎟⎟⎟⎟⎠
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
I1I2I3· · ·I4I5I6· · ·I7I8I9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
=
⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝
V1V2V3· · ·V4V5V6· · ·V7V8V9
⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠
We can use the formula for the inverse of matrix partitioned into four parts twice on this matrix partitionedinto nine parts. But it may be a bit much to expect to easily obtain explicit formulae the way we did forthe 2 × 2 case. . .
Note that we are only really interested in the bottom left corner (C31) of the inverse, given that I4, I5,I6, I7, I8, and I9 are always zero, and that we do not measure V1, V2, V3, V4, V5, and V6. Each experimentyields three measurements and thus three equations of the form
C31
⎛⎝ I1I2
I3
⎞⎠ =
⎛⎝ V7V8
V9
⎞⎠ .
By performing three experiments we can find all nine elements of the matrix C31. Each of these is apolynomial in the unknown leakage conductances g1, g2, g3, g4, g5, g6, g7, g8, and g9 (or rather, we cancross-multiply to obtain nine such polynomials).
The part of the inverse of this conductance matrix that we need is the lower left corner, C31. Using thedecomposition rule for partitioned 2 × 2 matrices twice, we get
C31 = G−133 G32(G22 − G23G−133 G21)−1G21(G11 − G12(G22 − G23G−133 G21)−1G21
)−1
Note that the term (G22 −G23G−133 G21)−1G21 appears twice. This can be exploited to save on computation.
6
Binder11Binder10Binder9Binder6Binder5Binder4Binder3Binder2Binder1scattering_CIcomputational_imaging_new.pdfBinder3.pdfcomputational_imaging.pdfBinder5.pdfBinder2.pdfBinder1.pdfcomputational_imaging_new.pdftemp.pdf
temp.pdf
temp.pdf
temp1.pdftemp2.pdftemp3.pdftemp5.pdftemp7.pdftemp8.pdftemp9.pdf
projection1.pdfprojection2.pdf
projection1.pdfprojection2.pdf
temp.pdf
recon_figures
figures-AO1
threed_grid
threed_bottom
leakage
threed_grid
leakage
diaphanography_update
APL_Cover
threed_perspective