introduction to inverse problems - uni-frankfurt.deharrach/talks/2015...introduction to inverse...
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Introduction to Inverse Problems
Bastian von [email protected]
Chair of Optimization and Inverse Problems, University of Stuttgart, Germany
Department of Computational Science & EngineeringYonsei University
Seoul, South Korea, April 3, 2015.
B. Harrach: Introduction to Inverse Problems
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Motivation and examples
B. Harrach: Introduction to Inverse Problems
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Laplace’s demon
Laplace’s demon: (Pierre Simon Laplace 1814)
”An intellect which (. . . ) would know allforces (. . . ) and all positions of all items
(. . . ), if this intellect were also vast enoughto submit these data to analysis, (. . . ); for
such an intellect nothing would beuncertain and the future just like the past
would be present before its eyes.”
B. Harrach: Introduction to Inverse Problems
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Computational Science
Computational Science / Simulation Technology:
If we know all necessary parameters, then we can numerically predictthe outcome of an experiment (by solving mathematical formulas).
Goals:
▸ Prediction
▸ Optimization
▸ Inversion/Identification
B. Harrach: Introduction to Inverse Problems
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Computational Science
Generic simulation problem:
Given input x calculate outcome y = F (x).
x ∈ X : parameters / inputy ∈ Y : outcome / measurements
F ∶ X → Y : functional relation / model
Goals:
▸ Prediction: Given x , calculate y = F (x).▸ Optimization: Find x , such that F (x) is optimal.
▸ Inversion/Identification: Given F (x), calculate x .
B. Harrach: Introduction to Inverse Problems
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Examples
Examples of inverse problems:
▸ Electrical impedance tomography
▸ Computerized tomography
▸ Image Deblurring
▸ Numerical Differentiation
0 0.5 10
2
4
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Electrical impedance tomography (EIT)
▸ Apply electric currents on subject’s boundary
▸ Measure necessary voltages
↝ Reconstruct conductivity inside subject.
B. Harrach: Introduction to Inverse Problems
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Electrical impedance tomography (EIT)
xImage
Fz→ y = F (x)Measurements
x : Interior conductivity distribution (image)y : Voltage and current measurements
Direct problem: Simulate/predict the measurements(from knowledge of the interior conductivity distribution)
Given x calculate F (x) = y !
Inverse problem: Reconstruct/image the interior distribution(from taking voltage/current measurements)
Given y solve F (x) = y !
B. Harrach: Introduction to Inverse Problems
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X-ray computerized tomography
Nobel Prize in Physiology or Medicine 1979:Allan M. Cormack and Godfrey N. Hounsfield(Photos: Copyright ©The Nobel Foundation)
Idea: Take x-ray images from several directions
DetectorDe
tector D
etector
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Computerized tomography (CT)
Image
Fz→
Drehung des Scanners
Pos
ition
im S
cann
er
100 200 300 400 500
600
500
400
300
200
100
0
1
2
3
4
Measurements
Direct problem: Simulate/predict the measurements(from knowledge of the interior density distribution)
Given x calculate F (x) = y !
Inverse problem: Reconstruct/image the interior distribution(from taking x-ray measurements)
Given y solve F (x) = y !
B. Harrach: Introduction to Inverse Problems
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Image deblurring
xTrue image
Fz→
y = F (x)Blurred image
Direct problem: Simulate/predict the blurred image(from knowledge of the true image)
Given x calculate F (x) = y !
Inverse problem: Reconstruct/image the true image(from the blurred image)
Given y solve F (x) = y !
B. Harrach: Introduction to Inverse Problems
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Numerical differentiation
0 0.5 10
2
4
xFunction
Fz→0 0.5 1
0
0.5
1
y = F (x)Primitive Function
Direct problem: Calculate the primitiveGiven x calculate F (x) = y !
Inverse problem: Calculate the derivativeGiven y solve F (x) = y !
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Ill-posedness
B. Harrach: Introduction to Inverse Problems
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Well-posedness
Hadamard (1865–1963): A problem is called well-posed, if
▸ a solution exists,
▸ the solution is unique,
▸ the solution depends continuously on the given data.
Inverse Problem: Given y solve F (x) = y !
▸ F surjective?
▸ F injective?
▸ F−1 continuous?
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Ill-posed problems
Ill-posedness: F−1 ∶ Y → X not continuous.
x ∈ X : true solutiony = F (x) ∈ Y : exact measurement
y δ ∈ Y : real measurement containing noise δ > 0,e.g. ∥yδ − y∥
Y≤ δ
For δ → 0
y δ → y , but (generally) F−1(y δ) /→ F−1(y) = x
Even the smallest amount of noise will corrupt the reconstructions.
B. Harrach: Introduction to Inverse Problems
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Numerical differentiation
Numerical differentiation example (h = 10−3)
0 0.5 1
0
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1
0 0.5 10
2
4
y(t) and y δ(t) y(t+h)−y(t)h and yδ(t+h)−yδ(t)
h
Differentiation seems to be an ill-posed (inverse) problem.
B. Harrach: Introduction to Inverse Problems
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Image deblurring
F↦
↧ add 0.1% noise
F−1↤
Deblurring seems to be an ill-posed (inverse) problem.
B. Harrach: Introduction to Inverse Problems
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Image deblurring
F↦
↧ add 1% noise
F−1↤
CT seems to be an ill-posed (inverse) problem.
B. Harrach: Introduction to Inverse Problems
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Compactness and ill-posedness
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Compactness
Consider the general problem
F ∶ X → Y , F (x) = y
with X , Y real Hilbert spaces.Assume that F is linear, bounded and injective with left inverse
F−1 ∶ F (X ) ⊆ Y → X .
Definition 1.1. F ∈ L(X ,Y ) is called compact, if
F (U) is compact for alle bounded U ⊆ X ,
i.e. if (xn)n∈N ⊂ X is a bounded sequence then (F (xn))n∈N ⊂ Ycontains a bounded subsequence.
B. Harrach: Introduction to Inverse Problems
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Compactness
Theorem 1.2. Let
▸ F ∈ L(X ,Y ) be compact and injective, and
▸ dimX =∞,
then the left inverse F−1 is not continuous, i.e. the inverse problem
Fx = y
is ill-posed.
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Compactness
Theorem 1.3. Every limit1 of compact operators is compact.
Theorem 1.4. If dimR(F ) <∞ then F is compact.
Corollary. Every operator that can be approximated1 by finitedimensional operators is compact.
1in the uniform operator topologyB. Harrach: Introduction to Inverse Problems
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Compactness
Theorem 1.5. Let F ∈ L(X ,Y ) possess an unbounded left inverseF−1, and let Rn ∈ L(Y ,X ) be a sequence with
Rny → F−1y for all y ∈R(F ).
Then ∥Rn∥→∞.
Corollary. If we discretize an ill-posed problem, the better wediscretize, the more unbounded our discretizations become.
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Compactness and ill-posedness
Discretization: Approximation by finite-dimensional operators.
Consequences for discretizing infinite-dimensional problems:
If an infinite-dimensional direct problem can be discretized1, then
▸ the direct operator is compact.
▸ the inverse problem is ill-posed, i.e. the smallest amount ofmeasurement noise may completely corrupt the outcome of the(exact, infinite-dimensional) inversion.
If we discretize the inverse problem, then
▸ the better we discretize, the larger the noise amplification is.
1in the uniform operator topologyB. Harrach: Introduction to Inverse Problems
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Examples
▸ The operator
F ∶ function ↦ primitive function
is a linear, compact operator.
↝ The inverse problem of differentiation is ill-posed.
▸ The operator
F ∶ exact image ↦ blurred image
is a linear, compact operator.
↝ The inverse problem of image deblurring is ill-posed.
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Examples
▸ In computerized tomography, the operator
F ∶ image ↦ measurements
is a linear, compact operator.
↝ The inverse problem of CT is ill-posed.
▸ In EIT, the operator
F ∶ image ↦ measurements
is a non-linear operator. Its Frechet derivative is a compactlinear operator.
↝ The (linearized) inverse problem of EIT is ill-posed.
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Regularization
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Numerical differentiation
Numerical differentiation example
0 0.5 1
0
0.5
1
0 0.5 10
2
4
y(t) and y δ(t) y(t+h)−y(t)h and yδ(t+h)−yδ(t)
h
Differentiation is an ill-posed (inverse) problem
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Regularization
Numerical differentiation:
▸ y ∈ C 2, C ∶= 2 supτ ∣g ′′(τ)∣ <∞, ∣y δ(t) − y(t)∣ ≤ δ ∀t
∣y ′(t) − y δ(t + h) − y δ(t)h
∣
≤ ∣y ′(x) − y(t + h) − y(t)h
∣
+ ∣y(t + h) − y(t)h
− y δ(t + h) − y δ(t)h
∣
≤ Ch + 2δ
h→ 0.
for δ → 0 and adequately chosen h = h(δ), e.g., h ∶=√δ.
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Numerical differentiation
Numerical differentiation example
0 0.5 10
2
4
0 0.5 10
2
4
0 0.5 10
2
4
y ′(t) yδ(t+h)−yδ(t)h
yδ(t+h)−yδ(t)h
with h very small with h ≈√δ
Idea of regularization: Balance noise amplification and approximation
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Regularization
Regularization of inverse problems:
▸ F−1 not continuous, so that generally F−1(yδ) /→ F−1(y) = x for δ → 0
▸ Rα continuous approximations of F−1
Rα → F−1 (pointwise) for α→ 0
Rα(δ)yδ → F−1y = x for δ → 0
if the parameter α = α(δ) is correctly chosen.
Inexact but continuous reconstruction (regularization)+ Information on measurement noise (parameter choice rule)= Convergence
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Prominent regularization methods
▸ Tikhonov regularization
Rα = (F ∗F + αI )−1F ∗
▸ Rα continuous, ∥Rα∥ ≤ 1√
α
▸ Rαy → F−1y for α → 0, y ∈R(F )▸ Rαy
δ minimizes
∥Fx − yδ∥2 + α ∥x∥2 → min!
▸ Truncated singular value decomposition (TSVD)
▸ Landweber method
▸ . . .
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Parameter choice ruleConvergence of Tikhonov-regularization
∥Rαy δ − F−1y∥ ≤ ∥Rα(y δ − y)∥´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶
≤∥Rα∥δ
+ ∥Rαy − F−1y∥´¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¹¶→0 for α→ 0
Choose α(δ) such that (for δ → 0)
▸ α(δ)→ 0
▸ ∥Rα(δ)∥ δ = δ√α(δ)
→ 0
then Rα(δ)yδ → F−1y . E.g., set α(δ) ∶= δ.
Exakt inversion does not converge, F−1y δ /→ F−1y .Tikhonov-regularization converges, Rδy
δ → F−1y .
Better parameter choice rule:Choose α such that ∥FRαyδ − yδ∥ ≈ δ (discrepancy principle)
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Image deblurring
x y = F x y δ
⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
F−1y δ
(F ∗F + δI )−1F ∗y δ
B. Harrach: Introduction to Inverse Problems
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Computerized tomography
x y = F x y δ
⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
F−1y δ
(F ∗F + δI )−1F ∗y δ
B. Harrach: Introduction to Inverse Problems
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Conclusions and remarks
Conclusions
▸ Inverse problems are of great importance in comput. science.(parameter identification, medical tomography, etc.)
▸ For ill-posed inverse problems, the best data-fit solutionsgenerally do not converge against the true solution.
▸ The regularized solutions do converge against the true solution.
Strategies for non-linear inverse problems F (x) = y :
▸ First linearize, then regularize.
▸ First regularize, then linearize.
A-priori information
▸ Regularization can be used to incorporate a-priori knowledge(promote sparsity or sharp edges, include stochastic priors, etc.)
B. Harrach: Introduction to Inverse Problems