9 citations · 20 across the 10 of their papers we have counts for
15 papers
Model-adapted Fourier sampling for generative compressed sensing
Aaron Berk, Simone Brugiapaglia, Yaniv Plan +3
We study generative compressed sensing when the measurement matrix is randomly subsampled from a unitary matrix (with the DFT as an important special case). It was recently shown t…
Embracing Off-the-Grid Samples
Oscar López, Özgür Yılmaz
Many empirical studies suggest that samples of continuous-time signals taken at locations randomly deviated from an equispaced grid (i.e., off-the-grid) can benefit signal acquisit…
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate
Michael P. Friedlander, Halyun Jeong, Yaniv Plan +1
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence. Ther…
On one-stage recovery for -quantized compressed sensing
Arman Arian, Ozgur Yilmaz
Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. When such a signal is acquired…
Deterministic partial binary circulant compressed sensing matrices
Arman Arian, Ozgur Yilmaz
Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representation. This is achieved by collecting…
RIP constants for deterministic compressed sensing matrices-beyond Gershgorin
Arman Arian, Ozgur Yilmaz
Compressed sensing (CS) is a signal acquisition paradigm to simultaneously acquire and reduce dimension of signals that admit sparse representations. This is achieved by collecting…