Compressive phase retrieval: Optimal sample complexity with deep generative priors

Advances in compressive sensing (CS) provided reconstruction algorithms of sparse signals from linear measurements with optimal sample complexity, but natural extensions of this methodology to nonlinear inverse problems have been met with potentially fundamental sample complexity bottlenecks. In par...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Communications on pure and applied mathematics 2024-02, Vol.77 (2), p.1147-1223
Hauptverfasser:	Hand, Paul, Leong, Oscar, Voroninski, Vladislav
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Complexity Convexity Inverse problems Machine learning Optimization Phase retrieval Signal reconstruction Sparsity
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!