The second day of ISMP started (for me) with the session I organized and chaired.

The first talk was by Michael Goldman on Continuous Primal-Dual Methods in Image Processing. He considered the continuous Arrow-Hurwitz method for saddle point problems

$\displaystyle \min_{u}\max_{\xi} K(u,\xi)$

with ${K}$ convex in the first and concave in the second variable. The continuous Arrow-Hurwitz method consists of solving

$\displaystyle \begin{array}{rcl} \partial_t u(t) &=& -\nabla_u K(u(t),\xi(t))\\ \partial_t \xi(t) &=& \nabla_\xi K(u(t),\xi(t)). \end{array}$

His talk evolved around the problem if ${K}$ comes from a functional which contains the total variation, namely he considered

$\displaystyle K(u,\xi) = -\int_\Omega u\text{div}(\xi) + G(u)$

with the additional constraints ${\xi\in C^1_C(\Omega,{\mathbb R}^2)}$ and ${|\xi|\leq 1}$. For the case of ${G(u) = \lambda\|u-f\|^2/2}$ he presented a nice analysis of the problem including convergence of the method to a solution of the primal problem and some a-posteriori estimates. This reminded me of Showalters method for the regularization of ill-posed problems. The Arrow-Hurwitz method looks like a regularized version of Showalters method and hence, early stopping does not seem to be necessary for regularization. The related paper is Continuous Primal-Dual Methods for Image Processing.

The second talk was given by Elias Helou and was on Incremental Algorithms for Convex Non-Smooth Optimization with Applications in Image Reconstructions. He presented his work on a very general framework for problems of the class

$\displaystyle \min_{x\in X} f(x)$

with a convex function ${f}$ and a convex set ${X}$. Basically, he abstracted the properties of the projected subgradient method. This consists of taking subgradient descent steps for ${f}$ followed by projection onto ${X}$ iteratively: With a subgradient ${g^n\in\partial f(x^n)}$ this reads as

$\displaystyle x^{n+1} = P_X(x^n -\alpha_n g^n)$

he extracted the conditions one needs from the subgradient descent step and from the projection step and formulated an algorithm which consists of successive application of an “optimality operator” ${\mathcal{O}_f}$ (replacing the subgradient step) and a feasibility operator ${\mathcal{F}_X}$ (replacing the projection step). The algorithm then reads as

$\displaystyle \begin{array}{rcl} x^{n+1/2} &=& \mathcal{O}_f(x^n,\alpha_n)\\ x^{n+1} &=& \mathcal{F}_x(x^{n+1/2} \end{array}$

and he showed convergence under the extracted conditions. The related paper is , Incremental Subgradients for Constrained Convex Optimization: a Unified Framework and New Methods.

The third talk was by Jerome Fehrenbach on Stripes removel in images, apllications in microscopy. He considered the problem of very specific noise which is appear in the form of stripes (and appears, for example, “single plane illumination microscopy”). In fact he considered a little more general case and the model he proposed was as follows: The observed image is

$\displaystyle u_{\text{OBS}} = u + n,$

i.e. the usual sum of the true image ${u}$ and noise ${n}$. However, for the noise he assumed that it is given by

$\displaystyle n = \sum_{j=1}^m \psi_j*\lambda_j,$

i.e. it is a sum of different convolutions. The ${\psi_j}$ are kind of shape-functions which describe the “pattern of the noise” and the ${\lambda_j}$ are samples of noise processes, following specific distributions (could be white noise realizations, impulsive noise or something else)-. He then formulated a variational method to identify the variables ${\lambda_j}$ which reads as

$\displaystyle \min \|\nabla(u_{\text{OBS}} - \sum_{j=1}^m \psi_j*\lambda_j)\|_1 + \sum_j \phi_j(\lambda_j).$

Basically, this is the usual variational approach to image denoising, but nor the optimization variable is the noise rather than the image. This is due to the fact that the noise has a specific complicated structure and the usual formulation with ${u = u_{\text{OBS}} +n}$ is not feasible. He used the primal-dual algorithm by Chambolle and Pock for this problem and showed that the method works well on real world problems.

Another theme which caught my attention here is “optimization with variational inequalities as constraints”. At first glance that sounds pretty awkward. Variational inequalities can be quite complicated things and why on earth would somebody considers these things as side conditions in optimization problems? In fact there are good reasons to do so. One reason is, if you have to deal with bi-level optimization problems. Consider an optimization problem

$\displaystyle \min_{x\in C} F(x,p) \ \ \ \ \ (1)$

with convex ${C}$ and ${F(\cdot,p)}$ (omitting regularity conditions which could be necessary to impose) depending on a parameter ${p}$. Now consider the case that you want to choose the parameter ${p}$ in an optimal way, i.e. it solves another optimization problem. This could look like

$\displaystyle \min_p G(x)\quad\text{s.t.}\ x\ \text{solves (1)}. \ \ \ \ \ (2)$

Now you have an optimization problem as a constraint. Now we use the optimality condition for the problem~(1): For differentiable ${F}$, ${x^*}$ solves~(1) if and only if

$\displaystyle \forall y\in C:\ \nabla_x F(x^*(p),p)(y-x^*(p))\geq 0.$

In other words: We con reformulate (2) as

$\displaystyle \min_p G(x)\quad\text{s.t.}\ \forall y\in C:\ \nabla_x F(x^*(p),p)(y-x^*(p))\geq 0. \ \ \ \ \ (3)$

And there it is, our optimization problem with a variational inequality as constraint. Here at ISMP there are entire sessions devoted to this, see here and here.