Inverse problem and optimization

Transcription

1 Inverse problem and optimization Laurent Condat, Nelly Pustelnik CNRS, Gipsa-lab CNRS, Laboratoire de Physique de l ENS de Lyon Decembre, 15th 2016

2 Inverse problem and optimization 2/36 Plan 1. Examples of inverse problems 2. Direct model: linear operator, noise 3. Ill-posed problem 4. Inversion 5. Regularization 6. Gradient descent

3 Inverse problem and optimization 3/36 Example: denoising Observations Denoised image

4 Inverse problem and optimization 3/36 Example: denoising

5 Inverse problem and optimization 3/36 Example: denoising

6 Inverse problem and optimization 4/36 Example: motion blur Observations Restored image

7 Inverse problem and optimization 4/36 Example: motion blur

8 Inverse problem and optimization 4/36 Example: motion blur

9 Inverse problem and optimization 5/36 Example: structured illumination microscopy

10 Inverse problem and optimization 6/36 Example: positron emission tomography

11 Inverse problem and optimization 6/36 Example: positron emission tomography? Measurements Reconstructed images

12 Inverse problem and optimization 7/36 Example: cartoon-texture decomposition? Observations Texture Cartoon

13 Inverse problem and optimization 8/36 Notations Image x R N 1 N 2 x = ( x n1,n 2 ) 1 n 1 N 1,1 n 2 N 2 Vector consisting of the values of the image of size N = N 1 N 2 arranged column-wise x R N (with N = N 1 N 2 ) x = ( x n )1 n N

14 Inverse problem and optimization 9/36 Direct model z = D α (Hx) x = (x n ) 1 n N R N : vector consisting of the (unknown) values of the original image of size N = N 1 N 2. z = (z j ) 1 j M R M : vector containing the observed values of size M = M 1 M 2. H R M N : matrix associated to a linear degradation operator. D α : R M R M : models other degradations such as nonlinear ones or the effect of the noise, parameterized by α (e.g. additive noise with variance α, Poisson noise with scaling parameter α).

15 Inverse problem and optimization 9/36 Direct model z = D α (Hx) x = (x n ) 1 n N R N : vector consisting of the (unknown) values of the original image of size N = N 1 N 2. z = (z j ) 1 j M R M : vector containing the observed values of size M = M 1 M 2. H R M N : matrix associated to a linear degradation operator. D α : R M R M : models other degradations such as nonlinear ones or the effect of the noise, parameterized by α (e.g. additive noise with variance α, Poisson noise with scaling parameter α).

16 Inverse problem and optimization 10/36 Direct model: convolution z = D α (Hx) z = D α (h x) where {h x}: convolution product with the Point Spread Function (PSF) h of size Q 1 Q 2. Link between h and H: Under zero-end conditions, unknown image x is zero outside its domain [0,N 1 1] [0,N 2 1], kernel h is zero outside its domain [0,Q 1 1] [0,Q 2 1].

17 Inverse problem and optimization 11/36 Direct model: convolution Link between h and H: Zero padding of x and h: extended image x e and kernel h e of size M 1 M 2 as { x e xi1,i i 1,i 2 = 2 if 0 i 1 N 1 1 and 0 i 2 N if N 1 i 1 M 1 1 and N 2 i 2 M 2 1, { hi e hi1,i 1,i 2 = 2 if 0 i 1 Q 1 1 and 0 i 2 Q if Q 1 i 1 M 1 1 and Q 2 i 2 M 2 1, This yields to (Hx) j1,j 2 = M 1 1 i 1 =0 M 2 1 i 2 =0 h e j 1 i 1,j 2 i 2 x e i 1,i 2 where j 1 {0,...,M 1 1} and j 2 {0,...,M 2 1}.

18 Inverse problem and optimization 12/36 Direct model: convolution Link between h and H: H = H 0 HM1 1 H M H1 H 1 H0 HM H2 H 2 H1 H0... H H0 H M1 1 H M1 2 H M1 3 where H j1 denotes a circulant matrix with M 2 columns such that hj e 1,0 hj e 1,M 2 1 hj e 1,M h e j 1,1 hj e H j1 = 1,1 hj e 1,0 hj e 1,M h e j 1, RM 2 M 2. hj e 1,M 2 1 hj e 1,M 2 2 hj e 1,M hj e 1,0

19 where X denotes the Fourier transform of x. Inverse problem and optimization 13/36 Direct model: convolution If H is a block-circulant matrix with circulant blocks, then where D: diagonal matrix, H = U DU U: unitary matrix (i.e, U = U 1 ) representing the discrete Fourier transform, denotes here the transpose conjugate. Efficient computation of Hx: Hx = U DU(U U)x = U DX

20 Inverse problem and optimization 14/36 Direct model: convolution Gaussian filter h Original image x F(h) F(x) {h x}

21 Inverse problem and optimization 14/36 Direct model: convolution Gaussian filter h Original image x F(h) F(x) {h x}

22 Inverse problem and optimization 14/36 Direct model: convolution Uniform filter h Original image x F(h) F(x) {h x}

23 Inverse problem and optimization 14/36 Direct model: convolution Uniform filter h Original image x F(h) F(x) {h x}

24 Inverse problem and optimization 15/36 Direct model: tomography (without noise) Tube of response j z = Hx Pixel n x = (x n ) 1 n N R N : vector consisting of the (unknown) values of the original image of size N = N 1 N 2. H = (H ji ) 1 j M,1 n N : probability to detect an event in the tube/line of response. z = (z j ) 1 j M R M : vector containing the observed values (sinogram).

25 Inverse problem and optimization 16/36 Direct model: compressed sensing z = Hx x = (x n ) 1 n N R N : vector consisting of the (unknown) values of the original image of size N = N 1 N 2. z = (z j ) 1 j M R M : vector containing the observed values (size M N). H = (H ji ) 1 j M,1 j N : random measurement matrix (size M N).

26 Inverse problem and optimization 17/36 Direct model: super-resolution ( b {1,...,B}) z b = D b TWx +ε b z: B multicomponent images at low-resolution (size M), x: (high-resolution) image to be recovered (size N), D b : downsampling matrix (size M N such that M < N), T: matrix associated to the blur (size N N), W : warp matrix (size N N), ε b N(0,σ 2 Id K ): noise often assumed to be a zero-mean white Gaussian additive noise.

27 Inverse problem and optimization 18/36 Direct model z = D α (Hx) x = (x n ) 1 n N R N : vector consisting of the (unknown) values of the original image of size N = N 1 N 2. z = (z j ) 1 j M R M : vector containing the observed values of size M = M 1 M 2. H R M N : matrix associated to a linear degradation operator D α : R M R M : models other degradations such as nonlinear ones or the effect of the noise, parameterized by α (e.g. additive noise with variance α, Poisson noise with scaling parameter α)

28 Inverse problem and optimization 19/36 Direct model: Gaussian noise z = D α (Hx) z = Hx +b where b: white additive Gaussian noise with variance α = σ 2. Original image Degraded image with σ = 10

29 Inverse problem and optimization 19/36 Direct model: Gaussian noise z = D α (Hx) z = Hx +b where b: white additive Gaussian noise with variance α = σ 2. Original image Degraded image σ = 30

30 Inverse problem and optimization 19/36 Direct model: Gaussian noise z = D α (Hx) z = Hx +b where b: white additive Gaussian noise with variance α = σ 2. Original image Degraded image σ = 50

31 Inverse problem and optimization 20/36 Direct model: Poisson noise z = D α (Hx) where D α : Poisson noise with scaling parameter α noise variance varies with image intensity. Original image Poisson noise α = 1

32 Inverse problem and optimization 20/36 Direct model: Poisson noise z = D α (Hx) where D α : Poisson noise with scaling parameter α noise variance varies with image intensity. Original image Poisson noise α = 0.1

33 Inverse problem and optimization 20/36 Direct model: Poisson noise z = D α (Hx) where D α : Poisson noise with scaling parameter α noise variance varies with image intensity. Original image Poisson noise α = 0.01

34 Inverse problem and optimization 21/36 Inverse problem Inverse problem : Find x the closest from x from observations. z = D α (Hx)? Observations z R M Restored image x R N

35 Inverse problem and optimization 22/36 Hadamard conditions The problem z = Hx is said to be well-posed if it fulfills the Hadamard conditions (1902) 1. existence of a solution, i.e. the range ranh of H is equal to R M, 2. uniqueness of the solution, i.e. the nullspace ker H of H is equal to {0}, 3. stability of the solution x relatively to the observation, i.e. ( (z,z ) ( R M) 2) z z 0 x(z) x(z ) 0.

36 Inverse problem and optimization 23/36 Hadamard conditions The problem z = Hx is said to be well-posed if it fulfills the Hadamard conditions 1. existence of a solution, i.e. every vector z in R M is the image of a vector x in R N, 2. uniqueness of the solution, i.e. if x(z) and x (z) are two solutions, then they are necessarily equal since x(z) x (z) belongs to kera, 3. stability of the solution x relatively to the observation, i.e. ensure that a small perturbation of the observed image leads to a slight variation of the recovered image.

37 Inverse problem and optimization 24/36 Inversion Inverse filtering (if M = N et H est inversible) x = H 1 z = H 1 (Hx +b) if additive noise b R M = x +H 1 b Remark : Closed form expression but noise amplification if H ill-conditioned (ill-posed problem).

38 Inverse problem and optimization 24/36 Inversion Inverse filtering (if M N and rank of H is N) x = (H H) 1 H z = (H H) 1 H (Hx +b) if additive noise b R M = x +(H H) 1 H b Remark : Closed form expression but noise amplification if H ill-conditioned (ill-posed problem).

39 Inverse problem and optimization 25/36 Regularization Variational approach where x Argmin x R N z Hx 2 2 : data-term, z Hx 2 2 +λω(x) Ω(x): regularization term (e.g. Ω(x) = x 2 2 ), λ 0: regularization parameter. Remarks If λ = 0: inverse filtering,

40 Inverse problem and optimization 26/36 Maximum A Posteriori (MAP) Let x and z be random vector realizations X and Z. Maximum A Posteriori (MAP) ˆx Argmax x R N µ X Z=z (x) find x that maximizes the posterior µ X Z=z (x)

41 Inverse problem and optimization 26/36 Maximum A Posteriori (MAP) Let x and z be random vector realizations X and Z. Maximum A Posteriori (MAP) ˆx Argmax x R N µ X Z=z (x) find x that maximizes the posterior µ X Z=z (x) Bayes rule max x R N µ X Z=z(x) max x R N µ Z X=x(z) µ X (x) min x R N { log(µz X=x (z)) log(µ X (x)) }

42 Inverse problem and optimization 26/36 Maximum A Posteriori (MAP) Let x and z be random vector realizations X and Z. Maximum A Posteriori (MAP) ˆx Argmax x R N µ X Z=z (x) find x that maximizes the posterior µ X Z=z (x) Bayes rule max µ X Z=z (x) max µ Z X=x (z) µ X (x) x R N x R N min x R N { log(µz X=x (z)) }{{} Data-term min x R N f 1 (x)+f 2 (x) } log(µ X (x)) }{{} A priori

43 Inverse problem and optimization 27/36 Data-term: Gaussian noise ( x R N ) f 1 (x) = log(µ Z X=x (z)) Let z = Hx +b with b N(0,α) Gaussian likelihood: µ Z X=x (z) = ( ) M 1 ((Hx) (i) z (i) ) 2 exp 2πα 2α i=1 Data-term: f 1 (x) = M i=1 1 2α ((Hx) i z i ) 2

44 Inverse problem and optimization 28/36 Data-term: Poisson noise ( x R N ) f 1 (x) = log(µ Z X=x (z)) Let z = D α (Hx) where D α Poisson noise with parameter α. Poisson likelihood: M exp ( α(hx) (i)) ( µ Z X=x (z) = α(hx) (i) ) z (i) z (i)! i=1 Data-term: f 1 (x) = M i=1 Ψ ( i (Hx) (i) ) αυ z (i) ln(αυ) if z (i) > 0 and υ > 0, ( υ R) Ψ i (υ) = αυ si z (i) = 0 and υ 0, + otherwise.

45 Inverse problem and optimization 29/36 Prior: Tikhonov /TV ( x R N ) f 2 (x) = log(µ X (x)) Tikhonov [Tikhonov, 1963] f 2 (x) = Lx 2 N 1 N 2 = {l x} 2 i,j avec l = i=1 j=

46 Inverse problem and optimization 30/36 Minimisation problem Variational approach where x Argmin x R N L(x) = f(z,hx): data-term, Ω(x): regularization term, λ 0: regularization parameter. L(x)+λΩ(x)

47 Inverse problem and optimization 31/36 Convex optimization? Let f : R N ],+ ]. An optimization problem consists in solving: x Argmin x R N f(x) x is a global solution if for every x R N, f( x) f(x), Course objectif: Build a sequence (x n ) n Z that converges to x. f x 0 x n x n+1 x x

48 Inverse problem and optimization 32/36 Gradient descent Illustration: Remarks: f is displayed with its level lines, Fermat rule: x Argmin x R N f(x) f( x) = 0 Solve a problem with N equations and N unknowns. Closed form expression for very few f. If no closed form expression possible iterative method.

49 Inverse problem and optimization 33/36 Solving mean square problem Find x Argmin x R N Hx z 2 2 with { H R M N z R M Optimality conditions f( x) = 0 H (H x z) = 0 x = (H H) 1 H z Difficulty: invert H H.

50 Inverse problem and optimization 34/36 Solving logistic regression Find x Argminlog(1+exp( yx) with x R y R Optimality conditions f( x) = 0 y exp( y x 1+exp( y x) = 0 Difficulty: no closed form expression.

51 Inverse problem and optimization 35/36 Gradient descent Illustration: Iterations: ( k N) x [k+1] = x [k] +γ [k] d [k] where { d [k] R N : descent direction, γ [k] > 0: step-size.