Compressed sensing using generalized polygon samplers

We propose new deterministic low-storage constructions of compressive sampling matrices based on classical finite-geometry generalized polygons. For the noiseless measurements case, we develop a novel exact-recovery algorithm for strictly sparse signals that utilizes the geometry properties of gener...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Gao, Kanke, Batalama, Stella N, Pados, D A, Suter, B W
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Belief propagation bipartite graphs Complexity theory Compressed sensing compressive sampling finite geometry generalized polygons Matching pursuit algorithms Noise Noise measurement Nyquist sampling Q measurement Sparse matrices sparse signals
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!