Program of ISCS Workshop
2-3 June 2025
Luxembourg City
Program at a Glance
Table of Contents
[Day 1] June 2
Lecture 1: Mathematics of Computational Imaging Systems, by Nelly Pustelnik (ENS Lyon, France)
1- Introduction to inverse problems in imaging
2- From sparsity-driven proximal algorithms to deep learning
2.1- Sparsity in image processing
2.2- Proximal algorithms for convex optimization
2.3- Proximal algorithms for nonconvex optimization
2.4- Neural networks and its link to proximal algorithms
3- Model based neural networks
3.1- Plug-and-play algorithms
3.2- Unfolded neural network
Lecture 2: Physics of Computational Imaging Systems, by Gail McConnell (Uni. of Strathclyde, Scotland)
1- Optical image formation
2- Light sources and detectors in optical microscopy
3- Contrast in optical microscopy
4- Advantages and disadvantages of labelling for optical microscopy
5- Common microscopy methods in optical microscopy
6- Current trends and opportunities in optical microscopy
7- Multiscale imaging
8- Considerations for designing an optical microscope, with emphasis on the Mesolens
9- Optical mesoscopy and its applications in biology and biomedicine
[Day 2] June 3
Track 1: DeepInverse: A Pytorch Library For Solving Imaging Inverse Problems With Deep Learning, by Julian Tachella (ENS Lyon, France), Matthieu Terris (INRIA, France), and Samuel Hurault (CNRS, France)
1- Introduction to the library and forward operators
2- Presentation of mini-projects
3- Iterative methods: explicit regularizations (TV, wavelets, etc.), and implicit regularizations (plug-and-play)
4- Sampling methods: Plug-and-play ULA and diffusion methods
5- Training: supervised and self-supervised losses, training details
6- Division of participants in small groups per mini-project
7- Coding session with organizers providing support
8- Short presentation of mini-projects by participants