Date

2025-Jan-26

On the Interface of Optimization and Deep Learning for Computational Imaging

Abstract:

This session focuses on computational imaging methods at the interface of optimization and deep learning. Recent advances aiming at pairing these two areas has led to significant advancements in image reconstruction and enhancement. Optimization techniques traditionally provided mathematically rigorous solutions for imaging problems, but they often struggled with high-dimensional data and non-linearities. Deep learning, particularly neural networks, has transformed this landscape by learning complex mappings from data, enabling faster and higher expressivity in image solutions. When combined, these approaches harness optimization algorithms to refine and guide the training of deep networks, resulting in robust models that are highly efficient for performing computational imaging tasks.

Organiser & chair: Audrey Repetti

Session Schedule

17:30 - 17:50
Invited talk: On the Interface of Optimization and Deep Learning for Computational Imaging
Audrey Repetti
17:50 - 18:10
Invited talk: True and False Monotone Neural Networks
Jean-Christophe Pesquet
18:10 - 18:30
Invited talk: Data-Driven Spatial Adaptivity for Regularising Inverse Problems
Sebastian Neumayer
18:30 - 18:50
Invited talk: Deep Image Regularisation for Poisson Inverse Problems via Mirror Descent
Luca Calatroni
18:50 - 19:10
Invited talk: (Deep) Machine Learning for Exoplanet Detection in Direct Imaging at High Contrast
Julien Mairal
19:10 - 20:30
Invited poster: Convergence Analysis of Plug-and-Play Methods for Image Inverse Problems
Samuel Hurault*
19:10 - 20:30
Invited poster: Stochastic Deep Restoration Priors for Imaging Inverse Problems
Chicago Park
19:10 - 20:30
Contributed poster: LoFi: Scalable Local Image Reconstruction with Implicit Neural Representation
Amirehsan Khorashadizadeh*
19:10 - 20:30
Contributed poster: Equivariant Plug-and-play Image Reconstruction
Thomas Moreau
19:10 - 20:30
Contributed poster: Variational Reconstruction of Parameter Maps in Medical Imaging and Materials Science
Gabriele Scrivanti*