Signal and image processing

In sparse recovery one aims to leverage the sparsity of some vector {u} to get a good reconstruction of this vector from a noisy and indirect measurement {u^{0}}. Sparsity can be expressed in two different ways: The analysis way and the synthesis way. For both of them you need a system of other vectors {v_{k}}.

  • The analysis way: Here you consider the inner products {\langle u,v_{k}\rangle} and the sparsity assumption is, that this sequence is sparse.
  • The synthesis way: Here you assume that there is sparse sequence of coefficients {\psi_{k}} such that {u=\sum_{k} \psi_{k}v_{k}}.

The first approach is called “analysis” since here one analyzes the vector {u} with the system of vectors. In the second approach, you use the vectors {v_{k}} to synthesize the vector {u}.

We will keep things very simple here and only consider a noisy observation (and not an indirect one). The recovery approach in the analysis way is then to find {u} as a solution of

\displaystyle \tfrac12\|u-u_{0}\| + \alpha R(\langle u,v_{k}\rangle)

while for the synthesis way you write {u = \sum_{k} \psi_{k}v_{k} = V\psi} (where we collected the vectors {v_{k}} as the columns of the matrix {V}) and solve

\displaystyle \tfrac12\|V\psi-u_{0}\| + \alpha R(\psi)

The functional {R} should enforce the sparsity of the vector of coefficients in the former case and the sparsity of {\psi} in the latter case. With the matrix {V} the analysis way reads as

\displaystyle \tfrac12\|u-u_{0}\| + \alpha R(V^{T}u)

One important observation is, that both approaches coincide if the matrix {V} is orthonormal: Since {V^{-1} = V^{T}} in this case, {u=V\psi} is equivalent to {\psi = V^{T}u}, i.e. {\psi_{k} = \langle u,v_{k}\rangle}. For other sets of vectors {v_{k}}, this is not true and it is not so clear, how the analysis and the synthesis approach are related. In this post I’d like to show one instance where both approaches do lead to results that are not even close to being similar.

1. The “ROF analysis” approach to denoising

If {u^{0}} is an image, the famous Rudin-Osher-Fatemi denoising model fits under the “analysis” framework: The vectors {v_{k}} are all the finite differences of neighboring pixels such that {V^{T}u = \nabla u} with the discrete gradient {\nabla u}. As sparsifying functional you take a mixed {2}{1} norm, i.e. {R(\nabla u) = \|\nabla u\|_{2,1} = \sum_{ij}\sqrt{(\partial_{1}u_{ij})^{2} + (\partial_{2}u_{ij})^{2}}}. The denoised {u} is the solution to

\displaystyle \min_{u} \tfrac12\|u-u^{0}\|^{2} + \alpha\|\nabla u\|_{2,1}

Thus, the model will lead to some denoised {u} with a sparse gradient. This sparse gradient is partly the reason for the success of the model in that it keeps edges, but is also responsible for the drawback of this approach: The denoised image will be mostly piesewise constant.

2. The “ROF synthesis” approach

As {V^{T} = \nabla}, the corresponding synthesis approach would be to solve

\displaystyle \min_{\psi}\tfrac12\|\nabla^{T}\psi - u^{0}\|^{2} + \alpha \|\psi\|_{2,1}

and the set {u = \nabla^{T}\psi}. In this approach, the denoised image will be a sparse linear combination of particular two-pixel images. If you think about that, it does not really sound like a good idea to approximate an image by a sparse linear combination of two-pixel images. (One technicality: The operator {\nabla^{T}} is not onto – {\nabla} has a non-trivial kernel, consisting of the constant images and hence, all images in the range of {\nabla^{T}} have zero mean. Thus, to get something remotely close to {u^{0}} one should subtract the mean of {u^{0}} before the reconstruction and add it back afterwards.)

Here are some results:



Note that the results actually fit to what we would have predicted: For larger {\alpha} we get a “sparser gradient” (less edges, less variation) for the analysis approach, and “sparser two-pixel combinations” for the synthesis approach. In fact, for the synthesis approach to give something that is remotely close to the image, one needs quite a lot two-pixel images, i.e. a very low sparsity.

In conclusion, one should not consider the analysis and synthesis approach to be something similar.

Incidentally, the dual of the analysis approach

\displaystyle \min_{u} \tfrac12\|u-u^{0}\|^{2} + \alpha\|\nabla u\|_{2,1}

can be written as

\displaystyle \min_{\|\phi\|_{2,\infty}\leq\alpha}\tfrac12\|\nabla^{T}\phi-u^{0}\|^{2} = \min_{\phi}\tfrac12\|\nabla^{T}\phi-u^{0}\|^{2} + I_{\|\cdot\|_{2,\infty}\leq\alpha}(\phi)

which looks related to the synthesis approach (one basically replaces the {2,1}-norm by its conjugate function). Actually, a similar relation between the dual of the analysis approach and the synthesis approach always holds.


Currently I am at the SIAM Imaging conference in Hong Kong. It’s a great conference with great people at a great place. I am pretty sure that this will be the only post from here, since the conference is quite intense. I just wanted to report on two ideas that have become clear here, although, they are both pretty easy and probably already widely known, but anyway:

1. Non-convex + convex objective

There are a lot of talks that deal with optimization problems of the form

\displaystyle  \min_u F(u) + G(u).

Especially, people try to leverage as much structure of the functionals {F} and {G} as possible. Frequently, there arises a need to deal with non-convex parts of the objective, and indeed, there are several approaches around that deal in one way or another with non-convexity of {F} or even {F+G}. Usually, in the presence of an {F} that is not convex, it is helpful if {G} has favorable properties, e.g. that still {F+G} is bounded from below, coercive or even convex again. A particularly helpful property is strong convexity of {G} (i.e. {G} stays convex even if you subtract {\epsilon/2\|\cdot\|^2} from it). Here comes the simple idea: If you already allow {F} to be non-convex, but only have a {G} that is merely convex, but not strongly so, you can modify your objective to

\displaystyle  \underbrace{F(u) - \tfrac\epsilon2\|u\|^2}_{\leftarrow F(u)} + \underbrace{G(u) + \tfrac\epsilon2\|u\|^2}_{\leftarrow G(u)}

for some {\epsilon>0}. This will give you strong convexity of {G} and an {F} that is (often) theoretically no worse than it used to be. It appeared to me that this is an idea that Kristian Bredies told me already almost ten years ago and which me made into a paper (together with Peter Maaß) in 2005 which got somehow delayed and published no earlier than 2009.

2. Convex-concave saddle point problems

If your problem has the form

\displaystyle  \min_u F(u) + G(Ku)

with some linear operator {K} and both {F} and {G} are convex, it has turned out, that it is tremendously helpful for the solution to consider the corresponding saddle point formulation: I.e. using the convex conjugate {G^*} of {G}, you write

\displaystyle  \min_u \max_v F(u) + \langle Ku, v\rangle -G^*(v).

A class of algorithms, that looks like to Arrow-Hurwicz-method at first glance, has been sparked be the method proposed by Chambolle and Pock. This method allows {F} and {G} to be merely convex (no smoothness or strong convexity needed) and only needs the proximal operators for both {F} and {G^*}. I also worked on algorithms for slightly more general problems, involving a reformulation of the saddle point problem as a monotone inclusion, with Tom Pock in the paper An accelerated forward-backward algorithm for monotone inclusions and I also should mention this nice approach by Bredies and Sun who consider another reformulation of the monotone inclusion. However, in the spirit of the first point, one should take advantage of all the available structure in the problem, e.g. smoothness of one of the terms. Some algorithm can exploit smoothness of either {F} or {G^*} and only need convexity of the other term. An idea, that has been used for some time already, to tackle the case if {F}, say, is a sum of a smooth part and a non-smooth part (and {G^*} is not smooth), is, to dualize the non-smooth part of {F}: Say we have {F = F_1 + F_2} with smooth {F_1}, then you could write

\displaystyle  \begin{array}{rcl}  &\min_u\max_v F_1(u) + F_2(u) + \langle Ku, v\rangle -G^*(v)\\ & \qquad= \max_u \min_{v,w} F_1(u) + \langle u,w\rangle + \langle Ku, v\rangle -G^*(v) - F_2^*(w) \end{array}

and you are back in business, if your method allows for sums of convex functions in the dual. The trick got the sticky name “dual transportation trick” in a talk by Marc Teboulle here and probably that will help, that I will not forget it from now on…

I fell a little bit behind on reporting on my new preprints. In this posts I’ll blog on two closely related ones; one of them already a bit old, the other one quite recent:

The papers are

As clear from the titles, both papers treat a similar method. The first paper contains all the theory and the second one has few particularly interesting applications.

In the first paper we propose to view several known algorithms such as the linearized Bregman method, the Kaczmarz method or the Landweber method from a different angle from which they all are special cases of another algorithm. To start with, consider a linear system

\displaystyle Ax=b

with {A\in{\mathbb R}^{m\times n}}. A fairly simple and old method to solve this, is the Landweber iteration which is

\displaystyle x^{k+1} = x^k - t_k A^T(Ax^k-b).

Obviously, this is nothing else than a gradient descent for the functional {\|Ax-b\|_2^2} and indeed converges to a minimizer of this functional (i.e. a least squares solution) if the stepsizes {t_k} fulfill {\epsilon\leq t_k\leq 2\|A\|^{-2} - \epsilon} for some {\epsilon>0}. If one initializes the method with {x^0=0} it converges to the least squares solution with minimal norm, i.e. to {A^\dag b} (with the pseudo-inverse {A^\dag}).

A totally different method is even older: The Kaczmarz method. Denoting by {a_k} the {k}-th row of {A} and {b_k} the {k}-th entry of {b} the method reads as

\displaystyle x^{k+1} = x^k - a_{r(k)}^T\frac{a_{r(k)}\cdot x^k - b_k}{\|a_{r(k)}\|_2^2}

where {r(k) = (k\mod m) +1} or any other “control sequence” that picks up every index infinitely often. This method also has a simple interpretation: Each equation {a_k\cdot x = b_k} describes a hyperplane in {{\mathbb R}^n}. The method does nothing else than projecting the iterates orthogonally onto the hyperplanes in an iterative manner. In the case that the system has a solution, the method converges to one, and if it is initialized with {x^0=0} we have again convergence to the minimum norm solution {A^\dag b}.

There is yet another method that solves {Ax=b} (but now it’s a bit more recent): The iteration produces two sequences of iterates

\displaystyle \begin{array}{rcl} z^{k+1} & = &z^k - t_k A^T(Ax^k - b)\\ x^{k+1} & = &S_\lambda(z^{k+1}) \end{array}

for some {\lambda>0}, the soft-thresholding function {S_\lambda(x) = \max(|x|-\lambda,0)\mathrm{sgn}(x)} and some stepsize {t_k}. For reasons I will not detail here, this is called the linearized Bregman method. It also converges to a solution of the system. The method is remarkably similar, but different from, the Landweber iteration (if the soft-thresholding function wouldn’t be there, both would be the same). It converges to the solution of {Ax=b} that has the minimum value for the functional {J(x) = \lambda\|x\|_1 + \tfrac12\|x\|_2^2}. Since this solution of close, and for {\lambda} large enough identical, to the minimum {\|\cdot\|_1} solution, the linearized Bregman method is a method for sparse reconstruction and applied in compressed sensing.

Now we put all three methods in a joint framework, and this is the framework of split feasibility problems (SFP). An SFP is a special case of a convex feasibility problems where one wants to find a point {x} in the intersection of multiple simple convex sets. In an SFP one has two different kinds of convex constraints (which I will call “simple” and “difficult” in the following):

  1. Constraints that just demand that {x\in C_i} for some convex sets {C_i}. I call these constraints “simple” because we assume that the projection onto each {C_i} is simple to obtain.
  2. Constraints that demand {A_ix\in Q_i} for some matrices {A_i} and simple convex sets {Q_i}. Although we assume that projections onto the {Q_i} are easy, these constraints are “difficult” because of the presence of the matrices {A_i}.

If there were only simple constraints a very basic method to solve the problem is the methods of alternating projections, also known as POCS (projection onto convex sets): Simply project onto all the sets {C_i} in an iterative manner. For difficult constraints, one can do the following: Construct a hyperplane {H_k} that separates the current iterate {x^k} from the set defined by the constraint {Ax\in Q} and project onto the hyperplane. Since projections onto hyperplanes are simple and since the hyperplane separates we move closer to the constraint set and this is a reasonable step to take. One such separating hyperplane is given as follows: For {x^k} compute {w^k = Ax^k-P_Q(Ax^k)} (with the orthogonal projection {P_Q}) and define

\displaystyle H_k = \{x\ : (A^Tw^k)^T\cdot x \leq (A^Tw^k)^T\cdot x^k - \|w^k\|_2^2\}.

Illustration of projections onto convex sets and separating hyperplanes

Illustration of projections onto convex sets and separating hyperplanes

Now we already can unite the Landweber iteration and the Kaczmarz method as follows: Consider the system {Ax=b} as a split feasibility problem in two different ways:

  1. Treat {Ax=b} as one single difficult constraint (i.e. set {Q=\{b\}}). Some calculations show that the above proposed method leads to the Landweber iteration (with a special stepsize).
  2. Treat {Ax=b} as {m} simple constraints {a_i\cdot x = b_i}. Again, some calculations show that this gives the Kaczmarz method.

Of course, one could also work “block-wise” and consider groups of equations as difficult constraints to obtain “block-Kaczmarz methods”.

Now comes the last twist: By adapting the term of “projection” one gets more methods. Particularly interesting is the notion of Bregman projections which comes from Bregman distances. I will not go into detail here, but Bregman distances are associated to convex functionals {J} and by replacing “projection onto {C_i} or hyperplanes” by respective Bregman projections, one gets another method for split feasibility problems. The two things I found remarkable:

  • The Bregman projection onto hyperplanes is pretty simple. To project some {x^k} onto the hyperplane {H = \{x\ :\ a^T\cdot x\leq \beta\}}, one needs a subgradient {z^k\in\partial J(x^k)} (in fact an “admissible one” but for that detail see the paper) and then performs

    \displaystyle x^{k+1} = \nabla J^*(z^k - t_k a)

    ({J^*} is the convex dual of {J}) with some appropriate stepsize {t_k} (which is the solution of a one-dimensional convex minimization problem). Moreover, {z^{k+1} = z^k - t_k a} is a new admissible subgradient at {x^{k+1}}.

  • If one has a problem with a constraint {Ax=b} (formulated as an SFP in one way or another) the method converges to the minimum-{J} solution of the equation if {J} is strongly convex.

Note that strong convexity of {J} implies differentiability of {J^*} and Lipschitz continuity of {\nabla J} and hence, the Bregman projection can indeed be carried out.

Now one already sees how this relates to the linearized Bregman method: Setting {J(x) = \lambda\|x\|_1 + \tfrac12\|x\|_2^2}, a little calculation shows that

\displaystyle \nabla J^*(z) = S_\lambda(z).

Hence, using the formulation with a “single difficult constraint” leads to the linearized Bregman method with a specific stepsize. It turns out that this stepsize is a pretty good one but also that one can show that a constant stepsize also works as long as it is positive and smaller that {2\|A\|^{-2}}.

In the paper we present several examples how one can use the framework. I see one strengths of this approach that one can add convex constraints to a given problem without getting into any trouble with the algorithmic framework.

The second paper extends a remark that we make in the first one: If one applies the framework of the linearized Bregman method to the case in which one considers the system {Ax=b} as {m} simple (hyperplane-)constraints one obtains a sparse Kaczmarz solver. Indeed one can use the simple iteration

\displaystyle \begin{array}{rcl} z^{k+1} & = &z^k - a_{r(k)}^T\frac{a_{r(k)}\cdot x^k - b_k}{\|a_{r(k)}\|_2^2}\\ x^{k+1} & = &S_\lambda(z^{k+1}) \end{array}

and will converge to the same sparse solution as the linearized Bregman method.

This method has a nice application to “online compressed sensing”: We illustrate this in the paper with an example from radio interferometry. There, large arrays of radio telescopes collect radio emissions from the sky. Each pair of telescopes lead to a single measurement of the Fourier transform of the quantity of interest. Hence, for {k} telescopes, each measurement gives {k(k-1)/2} samples in the Fourier domain. In our example we used data from the Very Large Array telescope which has 27 telescopes leading to 351 Fourier samples. That’s not much, if one want a picture of the emission with several ten thousands of pixels. But the good thing is that the Earth rotates (that’s good for several reasons): When the Earth rotates relative to the sky, the sampling pattern also rotates. Hence, one waits a small amount of time and makes another measurement. Commonly, this is done until the earth has made a half rotation, i.e. one complete measurement takes 12 hours. With the “online compressed sensing” framework we proposed, one can start reconstructing the image as soon the first measurements have arrived. Interestingly, one observes the following behavior: If one monitors the residual of the equation, it goes down during iterations and jumps up when new measurements arrive. But from some point on, the residual stays small! This says that the new measurements do not contradict the previous ones and more interestingly this happened precisely when the reconstruction error dropped down such that “exact reconstruction” in the sense of compressed sensing has happened. In the example of radio interferometry, this happened after 2.5 hours!

Reconstruction by online compressed sensing

Reconstruction by online compressed sensing

You can find slides of a talk I gave at the Sparse Tomo Days here.

In this post I will explore a bit the question on how to calculate the discrete gradient and the discrete divergence of an image and a vector field, respectively.

Let {u_0\in{\mathbb R}^{N\times M}} be a discrete image, i.e. {u_0(i,j)} denotes the gray value at the {i,j}-th pixel. The famous total variation denoising amounts to minimizing the functional

\displaystyle \Phi(u) = \tfrac12 \int (u-u_0)^2 + \lambda\int|\nabla u|

where the integral shall be understood as summation, {\nabla} stand for the gradient, i.e. the vector of the partial derivatives, and {|\cdot|} stands for the euclidean absolute value.

When using primal-dual methods, it is of crucial importance, that the used operators for the gradient and the divergence are numerically adjoint (up to the minus sign). That is, the numerical operations are adjoint in the following sense: If grad is the operation for the gradient and div is the operation for the divergence, then it should hold for any variables {u} and {v} of suitable size and with gradu = grad(u), divv = div(v) that sum(gradu(:).*v(:)) and -sum(u(:).*divv(:)) are equal up to numerical precision. Due to the boundary treatment, the internal MATLAB operations gradient and divergence do not fulfill this requirement.

The most common discretization of the gradient uses discrete forward differences and a constant padding at the boundary (which means that Neumann boundary values are applied). In formula, this reads as

\displaystyle (D_xu)_{i,j} = \begin{cases} u_{i+1,j} - u_{i,j} & i<N\\ 0 & i=N \end{cases} \qquad (D_yu)_{i,j} = \begin{cases} u_{i,j+1} - u_{i,j} & j<M\\ 0 & j=M \end{cases}

The respective adjoints are backward differences with zero boundary treatment (check it!). Apparently, there are many different ways to implement this routines (and the respective adjoints) in MATLAB. Here are four of them:

  1. For loops: Run through all pixels in two for-loops and assign the difference to the output. Of course, one should preallocate the output prior to the loop. But you may probably know the first two rules of MATLAB coding? If not, here they are: 1. Avoid for-loops. 2. Avoid for-loops, seriously. I put the routines into extra functions and created anonymous function to call the gradient and the divergence as
    grad = @(u) cat(3,dxp(u),dyp(u));
    div = @(V) dxm(V(:,:,1)) + dym(V(:,:,2));
  2. Shift and subtract: MATLAB is great in using vectors and matrices. And to avoid the for-loop one could also implement the forward difference in {x}-direction by shifting the matrix and subtract the original one, i.e. [u(:,2:end) u(:,end)] - u (and similarly for the other differences). Again, I wrote extra functions and used anonymous function as above.
  3. Small sparse matrices from the left and from the right: MATLAB is also pretty good with sparse matrices. Since the derivatives in {x}-direction only involve the subtraction of two elements in the same column, one can realize this by multiplying an image from the left with a sparse diagonal matrix with just two non-zero diagonals. Similarly, the derivative in {y}-direction can be realized by multiplying from the right with a suitable matrix. More precisely, this approach is realized by
    Dy = spdiags([-ones(M,1) ones(M,1)],[0 1],M,M);
    Dy(M,:) = 0;
    Dx = spdiags([-ones(N,1) ones(N,1)],[0 1],N,N);
    Dy(N,:) = 0;
    Dxu = Dx*u;
    Dyu = u*Dy';

    (check it). Note that the adjoint of {x}-derivative if simple the operation Dx'*u and the operation of the {y}-derivative is u*Dy. Together, the calculation of the gradient and the divergence was done by the anonymous functions

    grad = @(u) cat(3,u*Dx',Dy*u);
    div = @(V) V(:,:,1)*Dx + Dy'*V(:,:,2);
  4. Large sparse matrices: One could think about the following: Vectorize the image by U = u(:) (which amounts to stacking the columns above each other). Then assemble a large {NM\times NM} sparse matrix which has just two non-zero diagonals to do the forward (and other) differences. More precisely this can be done by
    (with Dx and Dy from above)

    DX = kron(Dx,speye(M));
    DY = kron(speye(N),Dy);
    DxU = DX*U;
    DyU = DY*U;

    Here, it is clear that the respective adjoint are just the multiplication with the transposed matrices. Here, the anonymous functions are

    grad = @(u) [DX*u DY*u];
    div = @(V) DX'*V(:,1) + DY'*V(:,2);

The different approaches have different pros and cons. Well, the for-loop only has cons: It is presumably slow and uses a lot indexing which easily leads to bugs. The shift-and-subtract method should go into an extra function to make it easy to use – but this is not necessarily a drawback. For the multiplication with the large matrix, one has to vectorize the image first and every time one wants to look at the result and need to do reshape(U,N,M). But let’s focus on speed: I implemented all methods, and let the run on square images of different sizes for 50 times (after a warmup) and measures the execution times with tic and toc. The assembly of the matrices did not enter the timing. Moreover, no parallel computation was used – just a single core. Finally, memory was not an issue since the larges matrices (of size {2500\times 2500\times 2}) only need roughly 100MB of memory.

Here is the table with the average times for one calculation of the gradient (in seconds):

{N} For-loop Shift-subtract left-right left
100 0.0004 0.0002 0.0001 0.0003
200 0.0018 0.0010 0.0005 0.0015
300 0.0057 0.0011 0.0014 0.0020
400 0.0096 0.0031 0.0022 0.0035
500 0.0178 0.0035 0.0030 0.0054
750 0.0449 0.0114 0.0097 0.0123
1000 0.0737 0.0189 0.0128 0.0212
1500 0.2055 0.0576 0.0379 0.0601
2000 0.3942 0.0915 0.0671 0.1136
2500 0.6719 0.1571 0.1068 0.1788

and here is the one for the divergences:

{N} For-loop Shift-subtract left-right left
100 0.0004 0.0003 0.0002 0.0002
200 0.0018 0.0015 0.0005 0.0008
300 0.0048 0.0016 0.0015 0.0012
400 0.0090 0.0036 0.0020 0.0022
500 0.0158 0.0057 0.0027 0.0035
750 0.0409 0.0132 0.0073 0.0069
1000 0.0708 0.0238 0.0130 0.0125
1500 0.2008 0.0654 0.0344 0.0370
2000 0.3886 0.1285 0.0622 0.0671
2500 0.6627 0.2512 0.1084 0.1361

As expected, the for-loop is clearly slower and also as expected, all methods basically scale quadratically (doubling {N} amounts to multiplying the running time by four) since the work per pixel is constant. A little surprisingly, the multiplication from the left and from the right is fastest and also consistently a little faster then multiplication from the left with the larger sparse matrix. I don’t know, why the results are that different for the gradient and for the divergence. Maybe this is related to my use on anonymous functions or the allocation of memory?

Another few notes to myself:

ISMP LogoISMP is over now and I’m already home. I do not have many things to report on from the last day. This is not due the lower quality of the talks but due to the fact that I was a little bit exhausted, as usual at the end of a five-day conference. However, I collect a few things for the record:

  • In the morning I visited the semi-planary by Xiaojun Chenon non-convex and non-smooth minimization with smoothing methods. Not surprisingly, she treated the problem

    \displaystyle \min_x f(x) + \|x\|_p^p

    with convex and smooth {f:{\mathbb R}^n\rightarrow{\mathbb R}} and {0<p<1}. She proposed and analyzed smoothing methods, that is, to smooth the problem a bit to obtain a Lipschitz-continuous objective function {\phi_\epsilon}, minimizing this and then gradually decreasing {\epsilon}. This works, as she showed. If I remember correctly, she also treated “iteratively reweighted least squares” as I described in my previous post. Unfortunately, she did not include the generalized forward-backward methods based on {\text{prox}}-functions for non-convex functions. Kristian and I pursued this approach in our paper Minimization of non-smooth, non-convex functionals by iterative thresholding and some special features of our analysis include:

    • A condition which excludes some (but not all) local minimizers from being global.
    • An algorithm which avoids this non-global minimizers by carefully adjusting the steplength of the method.
    • A result that the number of local minimizers is still finite, even if the problem is posed in {\ell^2({\mathbb N})} and not in {{\mathbb R}^n}.

    Most of our results hold true, if the {p}-quasi-norm is replaced by functions of the form

    \displaystyle \sum_n \phi_n(|x_n|)

    with special non-convex {\phi}, namely fulfilling a list of assumptions like

    • {\phi'(x) \rightarrow \infty} for {x\rightarrow 0} (infinite slope at {0}) and {\phi(x)\rightarrow\infty} for {x\rightarrow\infty} (mild coercivity),
    • {\phi'} strictly convex on {]0,\infty[} and {\phi'(x)/x\rightarrow 0} for {x\rightarrow\infty},
    • for each {b>0} there is {a>0} such that for {x<b} it holds that {\phi(x)>ax^2}, and
    • local integrability of some section of {\partial\phi'(x) x}.

    As one easily sees, {p}-quasi-norms fulfill the assumptions and some other interesting functions as well (e.g. some with very steep slope at {0} like {x\mapsto \log(x^{1/3}+1)}).

  • Jorge Nocedalgave a talk on second-order methods for non-smooth problems and his main example was a functional like

    \displaystyle \min_x f(x) + \|x\|_1

    with a convex and smooth {f}, but different from Xiaojun Chen, he only considered the {1}-norm. His talked is among the best planary talks I have ever attended and it was a great pleasure to listen to him. He carefully explained things and put them in perspective. In the case he skipped slides, he made me feel that I either did not miss an important thing, or understood them even though he didn’t show them He argued that it is not necessarily more expensive to use second order information in contrast to first order methods. Indeed, the {1}-norm can be used to reduce the number of degrees of freedom for a second order step. What was pretty interesting is, that he advocated semismooth Newton methods for this problem. Roland and I pursued this approach some time ago in our paper A Semismooth Newton Method for Tikhonov Functionals with Sparsity Constraints and, if I remember correctly (my notes are not complete at this point), his family of methods included our ssn-method. The method Roland and I proposed worked amazingly well in the cases in which it converged but the method suffered from non-global convergence. We had some preliminary ideas for globalization, which we could not tune enough to retain the speed of the method, and abandoned the topic. Now, that the topic will most probably be revived by the community, I am looking forward to fresh ideas here.

Today there are several things I could blog on. The first is the planary by Rich Baraniuk on Compressed Sensing. However, I don’t think that I could reflect the content in a way which would be helpful for a potential reader. Just for the record: If you have the chance to visit one of Rich’s talk: Do it!

The second thing is the talk by Bernd Hofmann on source conditions, smoothness and variational inequalities and their use in regularization of inverse problems. However, this would be too technical for now and I just did not take enough notes to write a meaningful post.

As a third thing I have the talk by Christian Clason on inverse problems with uniformly distributed noise. He argued that for uniform noise it is much better to use an {L^\infty} discrepancy term instead of the usual {L^2}-one. He presented a path-following semismooth Newton method to solve the problem

\displaystyle \min_x \frac{1}{p}\|Kx-y^\delta\|_\infty^p + \frac{\alpha}{2}\|x\|_2^2

and showed examples with different kinds of noise. Indeed the examples showed that {L^\infty} works much better than {L^2} here. But in fact it works even better, if the noise is not uniformly distributed but “impulsive” i.e. it attains bounds {\pm\delta} almost everywhere. It seems to me that uniform noise would need a slightly different penalty but I don’t know which one – probably you do? Moreover, Christian presented the balancing principle to choose the regularization parameter (without knowledge about the noise level) and this was the first time I really got what it’s about. What one does here is, to choose {\alpha} such that (for some {\sigma>0} which only depends on {K}, but not on the noise)

\displaystyle \sigma\|Kx_\alpha^\delta-y^\delta\|_\infty = \frac{\alpha}{2}\|x_\alpha^\delta\|_2^2.

The rational behind this is, that the left hand side is monotonically non-decreasing in {\alpha}, while the right hand side is monotonically non-increasing. Hence, there should be some {\alpha} “in the middle” which make both somewhat equally large. Of course, we do neither want to “over-regularize” (which would usually “smooth too much”) nor to “under-regularize” (which would not eliminate noise). Hence, balancing seems to be a valid choice. From a practical point of view the balancing is also nice because one can use the fixed-point iteration

\displaystyle \alpha^{n+1} = 2\sigma\frac{\|Kx_{\alpha^n}^\delta - y^\delta\|_\infty}{\|x_{\alpha_n}^\delta\|_2^2}

which converges in a few number of iterations.

Then there was the talk by Esther Klann, but unfortunately, I was late so only heard the last half…

Last but not least we have the talk by Christiane Pöschl. If you are interested in Total-Variation-Denoising (TV denoising), then you probably have heard many times that “TV denoising preserves edges” (have a look at the Wikipedia page – it claims this twice). What Christiane showed (in a work with Vicent Caselles and M. Novaga) that this claim is not true in general but only for very special cases. In case of characteristic functions, the only functions for which the TV minimizer has sharp edges are these so-called calibrated sets, introduced by Caselles et el. Building on earlier works by Caselles and co-workers she calculated exact minimizers for TV denoising in the case that the image consists of characteristic functions of two convex sets or of a single star shaped domain, that is, for a given set B she calculated the solution of

\displaystyle \min_u\int (u - \chi_B)^2dx + \lambda \int|Du|.

This is not is as easy as it may sound. Even for the minimizer for a single convex set one has to make some effort. She presented a nice connection of the shape of the obtained level-sets with the morphological operators of closing and opening. With the help of this link she derived a methodology to obtain the exact TV denoising minimizer for all parameters. I do not have the images right now but be assured that most of the time, the minimizers do not have sharp edges all over the place. Even for simple geometries (like two rectangles touching in a corner) strange things happen and only very few sharp edges appear. I’ll keep you posted in case the paper comes out (or appears as a preprint).

Christiane has some nice images which make this much more clear:

For two circles edges are preserved if they are far enough away from each other. If they are close, the area “in between” them is filled and, moreover, obey this fuzzy boundary. I remember myself seeing effects like this in the output of TV-solvers and thinking “well, it seems that the algorithm is either not good or not converged yet – TV should output sharp edges!”.


For a star-shaped shape (well, actually a star) the output looks like this. The corners are not only rounded but also blurred and this is true both for the “outer” corners and the “inner” corners.


So, if you have any TV-minimizing code, go ahead and check if your code actually does the right things on images like this!
Moreover, I would love to see similar results for more complicated extensions of TV like Total Generalized Variation, I treated here.





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