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ELEN Wavelets and Sparsity XIV
“Refractive Index Map Reconstruction in Optical Deflectometry
Using Total-Variation Regularization”
L. Jacques1, A. González2, E. Foumouo2 and P. Antoine2
1 Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM)
2 Institute of Condensed Matter and Nanosciences (IMCN)
University of Louvain, Louvain-la-Neuve, Belgium
ELEN Wavelets and Sparsity XIV
1. Introduction
ELEN Wavelets and Sparsity XIV
‣ World Before Sparsity: Human Readable Sensing (up to an orthogonal transform, e.g., Fourier)
Nyquist sampling (wrt bandlimitedness)
Examples: Photography, First MRIs, Interferometry, ...
Sparsity in Wavefields
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World SensingDevice Human
Optimized
(1/2)
ELEN Wavelets and Sparsity XIV
‣ Paradigm shift allowed by Sparsity:Computer “Readable” Sensing + Prior information (sparsity)
Examples: Seismology, Radio-Interferometry, Compressed Sensing, MRI,Computed Photography, ... (Many in this Workshop!)
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World SensingDevice Human
Sparsity in Wavefields
Optimized
(2/2)
ELEN Wavelets and Sparsity XIV
2. Optical Deflectometry
ELEN Wavelets and Sparsity XIV
Optical Computed Tomography Classification:‣ Absorption Tomography (X-ray tomography)
‣ Phase Tomography (using interferometry)
‣ and Deflection Tomography ...6
(Check Session 12, Tue 23)
ELEN Wavelets and Sparsity XIV
‣ Problem:Reconstructing the refractive index map of transparent materials from light deflection measurements under multiple orientations.
‣ Advantages of deflectometry:‣ Insensitive to vibrations (vs. interferometry)‣ Less sensitive to dispersive medium‣ Precision: up to 10nm flatness deviation on 50mm FOV
Interests: ‣ (transparent) object surface topology ‣ default detection in glass, crystal growth study
Deflectometric Framework:
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multifocal lens
(e.g., on window glass)
ELEN Wavelets and Sparsity XIV
∆(τ, θ) ��
R2
�∇n(x) · pθ
�δ(τ − x · pθ) d
2x
Mathematical Model:
(first order approximation)
ELEN Wavelets and Sparsity XIV
Experimentally: Phase-Shifting Schlieren“Coding light deviation in intensity variations”
Incoherent Backlight Source
LCD based programmable spatial filter:
Telecentric Imaging Optics
Pinhole Spatial Filter
CCD camera
m(x) = sin( 2πΛ x)
Object Observation
Area
(1/2)
ELEN Wavelets and Sparsity XIV
∆x = f tanα
sin( 2πΛ ∆x)
∆x
Intensity change
Object
Phase-Shifting
Algorithm
m(x) = sin( 2πΛ x)
Experimentally: Phase-Shifting Schlieren(2/2)
ELEN Wavelets and Sparsity XIV
3. Reconstruction Methods
ELEN Wavelets and Sparsity XIV
Deflectometric Central Slice Theorem:
with �n the 2-D FFT of n.
∆(τ, θ) ��
R2
�∇n(x) · pθ
�δ(τ − x · pθ) d
2x
Sensing Model:
Continuous Facts:
z(ω, θ) :=
�
R∆(τ, θ) e−iτωdτ = iω �n
�ω pθ
�
⇒ z(ω, θ) = iω �n�ω pθ
�
ELEN Wavelets and Sparsity XIV
Refractive Index Prior‣ Heterogenous transparent materials
with slowly varying refractive indexseparated by sharp interfaces
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(e.g., optical fibers)
The perfect “cartoon shape” model (BV, TV, ...)
“Sparse” gradient, i.e.,small Total Variation norm
�n�TV =
�
R�∇n(x)� dxn0
n1
ELEN Wavelets and Sparsity XIV
‣ Weighting:
‣ Polar to Cartesian interpolation:
Discretization of Measurements
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y(ω, θ) :=z(ω, θ) + ε(ω, θ)
iω= �n
�ω pθ
�+ η(ω, θ).
Given z = Fτ (∆(·, θ))
with ε additive white Gaussian noise (in τ and ω)
(ω, θ) ∼ ωpθ → (kx, ky)
Controlling error: Cartesian grid resolution
(could be NFFT)
y(ω, θ) → �y(kx, ky)
kx
ky
(noise error)
(interpolation error)
ExperimentalCoordinateSystem
(1/2)
ELEN Wavelets and Sparsity XIV
‣ Recording interpolated frequency pixel locations:
‣ Final Discrete Inverse Problem: Tomography (but “colored”)
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Discretization of Measurements
y = SF n+ η
K = {k1, · · · , kM/2} ⊂ R2
Selection Operator
2-D FT
noise (colored)interp. + meas.
y =
y(k1)
...y(kM/2)
∈ CM/2 � RM
∈ RN
Ill-posed: M � N
(2/2)
ELEN Wavelets and Sparsity XIV
Filtered Back Projection‣ In our notation: FBP amounts to solve
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argminu∈RN
�u�2 s.t. y = SFu
⇒ n∗ = (SF )†y = F ∗S∗ y
since SS∗ = FF ∗ = Id
ELEN Wavelets and Sparsity XIV
Regularized Whitened Reconstruction‣ Using TV prior and whitening noise: TV-L2
‣ Estimating W : ‣ Montecarlo on Normal Gaussian‣ Estimating std. dev. from
‣ Solving the reconstruction: ‣ Proximal methods‣ Iterative Chambolle-Pock algorithm
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argminu∈RN
�u�TV s.t. �W (y − SFu)�2 � �
with W = diag�σ(η1), · · · ,σ(ηM/2)
�−1
∆(τ, θ)
ELEN Wavelets and Sparsity XIV
4. Results
ELEN Wavelets and Sparsity XIV
Synthetic Images‣ Generating realistic maps:
‣ Using Image Reconstruction Toolbox (IRT1) for computing
‣ Adding noise and reconstructing FBP & TV-L2
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1 http://www.eecs.umich.edu/~fessler/code/
∆(τ, θ)
τ
θ
Varying #θ, Fixed #τ
(1/4)
ELEN Wavelets and Sparsity XIV
‣ No meas. noise‣ M/N = 10%
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Synthetic Images
4.94 dB 10.37 dB
(2/4)
ELEN Wavelets and Sparsity XIV
‣ Meas. noise: 11.51dB‣ M/N = 10%
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Synthetic Images
4.34 dB 5.85 dB
(3/4)
ELEN Wavelets and Sparsity XIV
‣ Measurement noise = 20 dB (another image)
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Synthetic Images
# angles
(4/4)
SNR
(dB
)
FBP
TV-L2
ELEN Wavelets and Sparsity XIV
Experimental Data‣ bundle of 10 fibers immersed
in an optical fluid‣ working on one z-slice (2-D problem)
‣ Origin of must be calibrated!‣ Noise is estimated on
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#τ = 696
τ
∆(τ, θ)
(1/3)
ELEN Wavelets and Sparsity XIV
‣
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Experimental Data
FBP
#θ = 60,M/N � 30%
(2/3)
ELEN Wavelets and Sparsity XIV
‣
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Experimental Data
FBP TV-L2
#θ = 60,M/N � 30%
(2/3)
ELEN Wavelets and Sparsity XIV
Do not forget calibration!‣ If misaligned origin
FBP and TV-L2 will look something like this:
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τ
(3/3)
ELEN Wavelets and Sparsity XIV
5. Conclusion
ELEN Wavelets and Sparsity XIV
Conclusion and Perspectives‣ Recent sparsity-driven reconstruction methods can be
applied to Optical Deflectometry.
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ELEN Wavelets and Sparsity XIV
Conclusion and Perspectives‣ Recent sparsity-driven reconstruction methods can be
applied to Optical Deflectometry.‣ Outside of easy synthetic Cartezian worlds,
hard time with gridding/interpolation/noise/calibration/...
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ELEN Wavelets and Sparsity XIV
Conclusion and Perspectives‣ Recent sparsity-driven reconstruction methods can be
applied to Optical Deflectometry.‣ Outside of easy synthetic Cartezian worlds,
hard time with gridding/interpolation/noise/calibration/... ‣ Same framework valid for:
Phase-Contrast X-Ray Tomography (& no first order approx!)
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ELEN Wavelets and Sparsity XIV
Conclusion and Perspectives‣ Recent sparsity-driven reconstruction methods can be
applied to Optical Deflectometry.‣ Outside of easy synthetic Cartezian worlds,
hard time with gridding/interpolation/noise/calibration/... ‣ Same framework valid for:
Phase-Contrast X-Ray Tomography (& no first order approx!) ‣ Future Improvements:
‣ Integrating positivity constraint‣ Using NFFT and reduce interpolation noise‣ Testing other reconstruction (Dantzig Fidelity ?)
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�Φ∗(y − Φn)�∞ � ρforces spatially stationary noise
ELEN Wavelets and Sparsity XIV
Thank you!