Large-scale Optimisation and Computational Imaging


Large-scale optimization problems arise in a variety of imaging tasks. Examples include dictionary learning, low-rank matrix recovery, blind deconvolution, and phase retrieval. Conventional approaches for solving many of these optimization problems involve designing algorithms that can effectively leverage a wide-variety of structural constraints. This session will provide an excellent opportunity for the wider signal processing and imaging community to come together and share recent developments, open challenges, and future directions in large-scale optimization methods suitable for analyzing imaging data.

Organisers: Gitta Kutyniok & Ulugbek Kamilov (Chair & Committee member)

Session Schedule

17:30 - 17:50 Committee member talk -
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
Ulugbek Kamilov
17:50 - 18:10 Invited talk -
Efficient Lip-1 spline networks for convergent PnP image reconstruction
Michael Unser
18:10 - 18:30 Invited talk -
Optimisation of deep neural networks under privacy constraints
Georgios Kaissis
18:30 - 18:50 Invited talk -
Hybrid Learning to Sense and Solve for Computational Imaging
Salman Asif
18:50 - 19:10 Invited talk -
Unfolded proximal denoising network for versatile plug-and-play algorithm
Audrey Repetti
19:10 - 20:30 Invited poster -
Signal processing with optical quadratic random sketches
Laurent Jacques
19:10 - 20:30 Contributed poster -
Block Delayed Majorize-Minimize Subspace Algorithm for Large Scale Image Restoration
Jean-Baptiste Fest*
19:10 - 20:30 Contributed poster -
A constrained optimization-based approach for multiphoton microscopy restoration
Ségolène Martin*
19:10 - 20:30 Contributed poster -
Diffusion-based Models meet Image Priors
Elrich Kobler*