In many areas of science, one has access to magnitude only measurements. Phase retrieval is the problem of recovering a signal from such measurements. Due to its practical significance in imaging science, numerous heuristics have been developed to solve such problems. Novel theoretical developments and exciting new algorithms and applications have led to a renewed interest in phase retrieval. The proposed minisymposium focuses on recent theoretical results showing the success of nonconvex optimization algorithms as well as spectacular recent research efforts in imaging applications. A special emphasis is on theoretical and algorithmic developments that are tightly related to real-world applications.

Denoising with spherically uniform neural layers

Dustin Mixon (Department of Mathematics, Ohio State University)

Fast Phase Retrieval from Windowed Fourier Measurements via Wigner Distribution Deconvolution + Angular Synchronization

Mark Iwen (Department of Mathematics, Michigan State University)

Nonconvex Sparse Blind Deconvolution: Geometry and Efficient Methods

John Wright (Department of Electrical Engineering, Columbia University)

Invariants for cryo-EM and multireference alignment

Tamir Bendory (Princeton University)

Burer-Monteiro for phase retrieval

Irene Waldspurger (CEREMADE (Université Paris-Dauphine))

Phase retrieval with structured measurements

Yonina Eldar (Department of EE, Technion, Israel Institute of Technology, Haifa)

Phase Retrieval Without Small-Ball Probability Assumptions

Felix Krahmer (Technical University of Munich, Department of Mathematics)

What are heuristic phase retrieval algorithms and why you should care

Veit Elser (Department of Physics, Cornell University)

Organizers:

Tamir Bendory (Princeton University)

Mahdi Soltanolkotabi (University of Southern California)

Keywords:

image deblurring, image reconstruction, inverse problems, machine learning, nonlinear optimization