Thursday,
September 30, 12:00 p.m. PSA 311
Kangyu (Connie) Ni
School of Mathematical and Statistical Sciences
Deterministic Compressed Sensing for Images with Chirps and
Reed-Muller Sequences
Abstract
A recent approach to compressed sensing using deterministic sensing
matrices formed from discrete frequency-modulated chirps or from
Reed-Muller sequences is extended to support efficient deterministic
reconstruction of signals that are less sparse than envisioned in the
original work. In particular, this allows the application of this
approach in imaging. The reconstruction algorithm developed for images
incorporates several new elements to improve computational complexity
and reconstruction fidelity in this application regime.
