The ISPGroup organizes regular seminars, reading and watching groups through, respectively,

ISP Seminars (ISPS)

The ISP Seminars (ISPS) aim at jointly motivating PhD students to present their research (or another research of interest) and inviting external speakers on more specialized topics. They usually focus on intelligent vision, inverse problems and sparsity, biomedical signal & medical image processing, community detection for hierarchical image segmentation, biomedical date analysis and medical imaging. Generally, the ISP Seminars take place in the Shannon seminar room (M-a105), Building Maxwell, 1st floor, place du Levant 3, Louvain-la-Neuve.

Upcoming seminars

Previous seminars

Exact Continuous Relaxations of $L_0$-Regularized Generalized Linear Models
Emmanuel Soubies (IRIT, CNRS, Université de Toulouse)
22 August 2024
Sparse generalized linear models are widely used in fields such as statistics, computer vision, signal/image processing and machine learning. The natural sparsity promoting regularizer is the $L_0$ pseudo-norm which is discontinuous and non-convex. In this talk, we will present the $L_0$-Bregman relaxation (B-Rex), a general framework to compute exact continuous relaxations of such $L_0$-regularized criteria. Although in general still non-convex, these continuous relaxations are qualified as exact in the sense that they let unchanged the set of global minimizer while enjoying a better optimization landscape. In particular, we will show that some local minimizers of the initial functional are eliminated by these relaxations. Finally, these properties will be illustrated on both sparse Kullback-Leibler regression and sparse logistic regression problems. This is joint work with M'hamed Essafri and Luca Calatroni.

External Web page: https://www.irit.fr/~Emmanuel.Soubies/

Euler seminar room (a.002) -- Thursday, 22 August 2024 at 14:00 (50 min.)

Computational Imaging: Restoration Deep Networks as Implicit Priors
Ulugbek Kamilov (Washington University, St. Louis, USA)
28 May 2024
Many interesting computational imaging problems can be formulated as imaging inverse problems. Since these problems are often ill-posed, one needs to integrate all the available prior knowledge for obtaining high-quality solutions. This talk will explore a series of techniques that leverage deep neural networks for image restoration as data-driven, implicit priors for images. The methods discussed originate from the well-known plug-and-play (PnP) methodology, known for its effectiveness in addressing imaging inverse problems. We will extend the conversation to the generalization of PnP methods, moving beyond traditional use of additive white Gaussian noise (AWGN) denoisers to include a variety of other restoration networks. This expansion not only enhances imaging performance but also offers the flexibility to train priors in the absence of clean data. Additionally, the talk will cover the theoretical underpinnings of using deep restoration networks and their applications in biomedical image reconstruction.

Biography: Ulugbek Kamilov is the Director of Computational Imaging Group and an Associate Professor of Electrical & Systems Engineering and Computer Science & Engineering at Washington University in St. Louis. He is currently a Visiting Professor at the Data Science Center at École Normale Supérieure in Paris. He obtained the BSc/MSc degree in Communication Systems in 2011 and the PhD degree in Electrical Engineering in 2015 from EPFL. He was a Visiting Research Faculty at Google Research in 2023-2024 and a Research Scientist at Mitsubishi Electric Research Laboratories in 2015-2017. He was an Exchange Student at Carnegie Mello University in 2008, Visiting Student Researcher at MIT in 2011, and Visiting Scholar at Stanford University in 2013.

He is a recipient of the NSF CAREER Award and the IEEE Signal Processing Society’s 2017 Best Paper Award. He was among 55 early-career researchers in the USA selected as a Fellow for the Scialog initiative on “Advancing Bioimaging” in 2021. His PhD thesis was selected as a finalist for the EPFL Doctorate Award in 2016. He was awarded Outstanding Teaching Award from the Department of Electrical & Systems Engineering at WashU in 2023. He is currently a Senior Member of the Editorial Board of IEEE Signal Processing Magazine and is on IEEE Signal Processing Society’s Bioimaging and Signal Processing Technical Committee. He has previously servedas an Associate Editor of IEEE Transactions on Computational Imaging and on IEEE Signal Processing Society’s Computational Imaging Technical Committee.

External Web page: https://engineering.wustl.edu/faculty/Ulugbek-Kamilov.html

Shannon seminar room (Maxwell) -- Tuesday, 28 May 2024 at 14:00 (45 min.)

AI in computational imaging: from algorithms to radio astronomy
Yves Wiaux (Heriot-Watt University Edinburgh, UK)
23 February 2024
Endowing advanced imaging instruments such as telescopes and medical scanners with an acute vision that enables them to probe the Universe or human body with precision is a complex mathematical endeavour. It requires solving challenging inverse problems for image formation from observed data. In this talk, we will dive into this field of computational imaging, and its specific application in radio astronomy, where algorithms are currently facing a multi-faceted challenge for the robust reconstruction of images at extreme resolution and dynamic range, and from extreme data volumes. We will discuss advanced algorithms at the interface of optimisation and deep learning theories, from SARA, an optimisation algorithm propelled by handcrafted regularisation priors, to AIRI, plug-and-play algorithm relying on learned regularisation denoisers, and the newborn deep neural network series R2D2, which can be interpreted as a learned version of the Matching Pursuit algorithm. If time allows, we will also briefly illustrate the transfer of such algorithms to medical imaging. Last but not least, we will take a few seconds to unveil Star Wars hidden facts and misconceptions.

Biography: Yves Wiaux received the MSc degree in Physics and the PhD degree in Theoretical Physics from the Université catholique de Louvain (UCL, Louvain-la-Neuve) in Belgium, in 1999 and 2002 respectively. He was a Senior Researcher at the Signal Processing Laboratories of the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland from 2003 to 2013, where he created the Biomedical and Astronomical Signal Processing (BASP) group. In 2013, he moved as an Associate Professor at the School of Engineering and Physical Sciences of Heriot-Watt University where he currently runs the BASP group. He was promoted to Professor at Heriot-Watt in 2016. He is also an Academic Guest at EPFL and a Honorary Fellow at the University of Edinburgh (UoE). Among other responsibilities, Prof. Wiaux chairs the “BASP Frontiers” Conference series, and is an Associate Editor of the IEEE “Transactions in Computational Imaging” (TCI) journal and of the “Royal Astronomy Society Techniques and Instruments” (RASTI) journal. Since 2010, he has led numerous research projects funded by both the Swiss National Science Foundation (SNSF) and the UK Research and Innovation Councils (UKRI). The ethos of Prof. Wiaux’s BASP group is to develop cutting-edge research in computational imaging, from theory and algorithms to applications in astronomy and medicine.

External Web page: https://researchportal.hw.ac.uk/en/persons/yves-wiaux

Euler Seminar Room (A002) -- Friday, 23 February 2024 at 11:00 (45 min.)

A full list of all seminars is available here

Location

If you come by car, we advise you to park in the “Baudouin 1er parking”. You’ll find a map of the pedestrian path joining this parking to the seminar room here (10’ walk).

IMAGINE: IMAGe processINg rEading group

Gauss’IP