This workshop is organized with the support of RT-MIA and ANR PostProdLeap.
Paris. Precise location to be announced later.
Designing efficient algorithms for ill-posed inverse problems in imaging science crucially relies on the choice of a prior on the solution. It has been a long-standing issue to design regularity priors that allow to use optimization algorithms having good convergence guarantees and producing sharp visual results. The Plug-and-Play framework implicitly regularizes inverse problems in optimization algorithms by substituting the regularizer, its gradient or its proximal operator by learned representations thereof. State-of-the-art restoration results have been obtained with denoisers parameterized by deep neural networks, and by generative models such as GANs, VAEs, Normalizing Flows or diffusion models. More recently the PnP framework has also been exploited in Bayesian imaging where it has been succesfully applied to MMSE estimation, uncertainty quantification among other Bayesian computations. However, these successes raise many issues related to the convergence of these schemes or their recovery guarantees. Moreover, the design of accurate denoising neural network architecture or the tuning of the hyperparameters involved are open problems that strongly affect the PnP restoration performance.
The aim of this workshop is to gather leading experts of Plug-and-Play image restoration in order to discuss recent advances on these topics and to draw new collaborations to tackle the currently open questions.
- Deep regularizers
- Non-convex optimization
- Automatic parameter tuning
- Neural network architectures
- Recovery guarantees
- Deep unfolding
Call for contributions
If you wish to propose a contribution to this workshop, please answer the following form:
- Regev Cohen (Verily Research)
- Julie Delon (MAP5, Université de Paris)
- Johannes Hertrich (Institut für Mathematik, Technische Universität Berlin)
- Zahra Kadkhodaie (Center for Data Science, New York University)
- Ulugbek Kamilov (Computational Imaging Group, Washington University)
- Matthieu Terris (Heriot-Watt University)