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 (Chair) & Ulugbek Kamilov (Committee member)

Session Schedule

17:30 - 17:50 Invited talk
Gitta Kutyniok
17:50 - 18:10 Invited talk
Michael Unser
18:10 - 18:30 Invited talk
Georgios Kaissis
18:30 - 18:50 Invited talk
Rene Vidal
18:50 - 19:10 Invited talk
Salman Asif
19:10 - 20:30 Invited poster
Olivier Leblanc
19:10 - 20:30 Committee member poster
Ulugbek Kamilov
19:10 - 20:30 Contributed poster
Jean-Baptiste Fest
19:10 - 20:30 Contributed poster
Ségolène Martin
19:10 - 20:30 Contributed poster
Audrey Repetti