Large-scale Optimisation and Computational Imaging
Abstract:
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*