Year: 2016
Type: article
Abstract: This paper addresses fundamental scaling issues that hinder phase retrieval (PR) in high dimensions. We show that, if the measurement matrix can be put into a generalized block-diagonal form, a large PR problem can be solved on separate blocks, at the cost of a few extra global measurements to merge the partial results. We illustrate this principle using two distinct PR methods, and discuss different design trade-offs. Experimental results indicate that this block-based PR framework can reduce computational cost and memory requirements by several orders of magnitude.
Source: IEEE Signal Processing Letters
Institutions: Centre National de la Recherche Scientifique, Université Paris Sciences et Lettres, Sadjad University, Institut Langevin, Collège de France, Laboratoire Kastler Brossel, École Normale Supérieure - PSL, Université Pierre-et-Marie-Curie, Laboratoire de Physique de l'Ecole Normale Supérieure, Sorbonne Paris Cité, Université Paris Diderot
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
Award: Paris Sciences et Lettres
OpenAlex ID: W2268825186