On May 21^st, 2025 at 10.00 am the seminar titled "Inverse Problems in the Age of AI" will take place at DEIB Alpha Room (Building 24).

Inverse problems involve reconstructing unknown physical quantities from indirect measurements. They appear in various fields, including medical imaging (e.g., MRI, Ultrasound, CT), material sciences and molecular biology (e.g., electron microscopy), as well as remote sensing just to name a few examples. While deep neural networks are currently able to achieve state-of-the-art performance in many imaging tasks, in this talk we argue that many inverse imaging problems cannot be solved convincingly using a black-box solution. Instead, they require a well-crafted combination of computational tools taking the underlying signal, the physical constraints and acquisition characteristics into account.

In the first part of the talk, we discuss the use of generative diffusion models for inverse problems and introduce INDigo, a novel INN-guided probabilistic diffusion algorithm for arbitrary image restoration tasks. INDigo combines the perfect reconstruction property of invertible neural networks (INNs) with the strong generative capabilities of pre-trained diffusion models. Specifically, we leverage the invertibility of the network to condition the diffusion process and in this way we generate high quality restored images consistent with the measurements. We also briefly discuss AI hallucination issues in this context.

In the second part of the talk, we discuss the unfolding techniques which is an approach that allows embedding priors and models in the neural network architecture. In this context we discuss the problem of monitoring the dynamics of large populations of neurons over a large area of the brain. Light-field microscopy (LFM), a type of scanless microscopy, is a particularly attractive candidate for high-speed three-dimensional (3D) imaging which is needed for monitoring neural activity. We review fundamental aspects of LFM and then present computational methods based on deep learning for neuron localization and activity estimation from light-field data.
Finally, we look at the multi-modal case and present an application in art investigation.
 Often X-ray images of Old Master paintings contain information of the visible painting and of concealed sub-surface design, we therefore introduce a model-based neural network capable of separating from the “mixed X-ray” the X-ray image of the visible painting and the X-ray of the concealed design.

This is joint work with A. Foust, P. Song, C. Howe, H. Verinaz, J. Huang, Di You and Y. Su, D. Siromani from Imperial College London, M. Rodrigues and W. Pu from University College London, I. Daubechies from Duke University, Barak Sober from the Hebrew University of Jerusalem and C. Higgitt and N. Daly from The National Gallery in London.
This work is sponsored by BBSRC, EPSRC and Wellcome Trust.

Pier Luigi Dragotti is Professor of Signal Processing in the Electrical and Electronic Engineering Department at Imperial College London and a Fellow of the IEEE. He received the Masters Degree (summa cum laude) from the University Federico II, Naples, Italy, in 1997 and PhD degree from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland in 2002. He has also held several visiting positions including at Stanford University, in 1996, at Bell Labs, Lucent Technologies, NJ in 2000 and at Massachusetts Institute of Technology (MIT) in 2011. Dragotti was Editor-in-Chief of the IEEE Transactions on Signal Processing (2018-2020), Technical Co-Chair for the European Signal Processing Conference in 2012 and Associate Editor of the IEEE Transactions on Image Processing from 2006 to 2009. He was a SPS Distinguished Lecturer in 2021-22. He was also an Elected Member of the IEEE Image, Video and Multidimensional Signal Processing Technical Committee, IEEE Signal Processing Theory and Methods Technical Committee and of the IEEE Computational Imaging Technical Committee. In 2011 he was awarded the prestigious ERC starting investigator award (consolidator stream).
His research interests include sampling theory and its applications, computational imaging and model-based machine learning.